Method for determining relationships between different pressure-related signals derivable from a human or animal body or body cavity

ABSTRACT

This invention describes a method for processing pressure signals derivable from locations inside or outside a human or animal body or body cavity. A major aspect of the invention relates to a method for determining relationships between different pressure-related signals with the purpose of obtaining signals predicting pressures inside a body or body cavity from pressure signals outside said body or body cavity. According to the invented method different formula-based relationships for specific types of signals and locations and sensor types are determined, which provides for a reference that can be applied on new and individual non-invasive pressure measurements. Thereby continuous pressure-related signals from a non-invasive source may be processed in a way that makes the continuous non-invasive pressure signals highly predictable of the pressures inside the body or body cavity.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a Divisional of application Ser. No. 10/895,387,filed on Jul. 21, 2004 , now U.S. Pat. No. 7,635,338, the entirecontents of which are hereby incorporated by reference and for whichpriority is claimed under 35 U.S.C. §120.

BACKGROUND OF THE INVENTION

1. Field of the Invention

Continuous monitoring of pressures from a human or animal body or bodycavity requires some kind of processing of the pressure measurements.Pressure measurements within a human being or animal are created frompressure waves created by the cardiac beats, though this fact is notnecessarily taken into account when measuring human or animal pressures.Pressure measurements may be derived from inside or outside a human oranimal body or body cavity. It may be preferable to place a pressuresensor outside a body or body cavity, but the problem is to obtainreliable pressure measurements from such sensor locations. Depending onthe type of such pressure measurements, signals related to pressuremeasurements may be garbled with noise, and the pressure difference frominside to outside the body cavity may be unknown. Pressure monitoringmay have a more widespread role than reflected by the current andexisting use. For example, various types of fluid flow valves are usedto drain excess fluid from a body cavity such as a human brain or spinalfluid body cavity. Related to the function of such valve devices,pressure monitoring has no or a minimal place.

2. Related Art

Continuous pressure monitoring has a widespread use concerning arterialblood pressure monitoring, ocular bulb pressure monitoring, intracranialpressure monitoring, lumbar cerebrospinal fluid pressure monitoring,urinary tract pressure monitoring, and gastrointestinal tract pressuremonitoring. Depending on how pressure measurements are performed,continuous pressure signals may be obtained. Most current and existingtechnologies solely use analogue signals, though modern data technologyallows such analogue signals to be converted into digital data signals.Some kind of signal processing may be applied to analogue as well asdigital pressure signals.

Though monitoring of pressures within a human body or body cavity hasbeen used for many decades, it is still unclear how pressuremeasurements should be processed to give best possible information fromsaid measurements. It is well known that pressures have a staticcomponent (mean pressure) and a dynamic component (pulse pressure),related to the fact that pressure waves within a human or animal body orbody cavity are created by cardiac beat-induced pressure waves. Duringpressure monitoring usually the static component is assessed whereas therole of the dynamic component is unclear. Technologies (e.g. by usingfast Fourier transformation of pressure signals) that measure thedynamic component of pressure measurements give no or minimal control asto whether the dynamic pressure changes are related to cardiacbeat-induced pressure waves or not.

Continuous pressure signals are processed by computation of meanpressure, usually computed as the sum of pressure levels divided by thenumbers of samples. It is not possible to evaluate whether said meanpressure is related to cardiac beat-induced pressure waves or not.According to current and prior art technology, evaluation whetherpressure measurements are associated with cardiac beat-induced pressurewaves is based on visual inspection of the pressure wave, or byinspection whether diastolic and systolic pressure values are different.Such evaluation may be very user-dependent and misleading. For practicalpurposes, continuous inspection of a pressure waveform during pressuremonitoring is impossible. Furthermore, waveform analysis according toprior art technology (e.g. fast Fourier analysis or modificationsthereof), does not allow assessment whether pressure waves are relatedto cardiac beats or not. Given bad signal quality such types of analysescan be very misleading. The inventor previously has described a methodfor processing continuous pressure signals in the following patentapplication: U.S. Ser. Nos. 10/283,245; 10/613,112; PCT/NO02/00164; andPCT/NO03/0029.

During pressure measurements it is preferable to use non-invasivesensors. The term non-invasive refers to the fact that the skin does notneed to be penetrated to measure pressure within a body or body cavity.There are numerous examples of non-invasive pressure monitoring. Placinga device on the skin, thereby sensing the arterial blood pressure withinthe body tissue, may monitor arterial blood pressure. TranscranialDoppler may provide signals that are transformable into pressure-relatedsignals indicative of intracranial pressure. Pressure-related signalsindicative of intracranial pressure may as well be measured by means ofa sensor device measuring air pressure within the outer ear channelafter air-tight sealing of the outer ear by some kind of closingmaterial to exclude interference from the atmospheric air pressure. Theproblem with so-called non-invasive pressure monitoring is that absolutepressure within the body or body cavity is unknown. The absolutepressure usually refers to the pressure difference between the pressureswithin the body or body cavity and the atmospheric pressure. Anothermajor problem is that it can be impossible to know whether the pressuremeasurements are good or bad, i.e. whether the quality of themeasurements or pressure signals are good or bad. This problem is atleast partly related to the lack of a standard of what might beconsidered a good (or bad) pressure measurement.

Since the 1950's fluid flow valves have been used to drain excess fluidfrom a human brain or spinal fluid body cavity. Such fluid flow valvesmay be controllable, i.e. the degree of fluid drainage is adjustable.Such devices include some kind of mechanically adjustable valves.Pressure monitoring has received no or minimal role as related to saiddrainage of fluid from a brain or spinal fluid body cavity. A majorcause is that pressure monitoring according to prior art technologymeasures absolute pressure, i.e. relative to atmospheric pressure.Changes in atmospheric pressure would change the zero pressure level andthus the measured pressure values. Sensor-related drift of zero pressurelevel also heavily affects the pressure measurements that are relativeto atmospheric pressure.

SUMMARY OF THE INVENTION

This invention provides technical solutions concerning at least tenfeatures related to pressure monitoring, as further defined in thepreamble of attached ten independent claim 1. For the sake of clarity,in this document it is referred to ten features of the invention. Thisshould, however, not be considered as a limitation of the scope of theinvention. Although the description and the drawings relate to all ofthe features of the invention, the claimed part of the inventionaccording to the attached claims is related to the third feature of theinvention.

Current and existing technology gives no or minimal opportunities forquality control whether a pressure signal incorporates pressure wavesrelated to the cardiac beats or pressure waves related to artifacts or acombination of cardiac beat-induced and artifact-induced pressure waves.It is well known from prior art that each cardiac beat causes a pressurewave that is transferred from the intra-cardiac and intra-arterialcompartments to the other human or animal body cavities. The shape andmagnitude of the individual pressure waves depend on the particular bodycavity, compartment of body cavity, the way of measuring the pressurewave, the absolute pressure level, and other factors such as posture andmedications. Depending on how a pressure signal is measured, thepressure signal may contain noise of various types and pressure wavesnot related to cardiac beats. The challenge is to identify the singlepressure waves corresponding to the cardiac beats, i.e. not tomisinterpret an artifact-induced pressure wave as a cardiac beat-inducedpressure wave. Said challenge is even greater when a pressure-relatedsignal is derived from a non-invasive source containing much noise inthe signal.

The first feature of this invention provides for a method for bestpossible differentiating between cardiac beat-induced pressure waves andpressure waves related to artifacts or a combination thereof. Morespecifically said first feature of the invention relates to: A methodfor processing continuous pressure-related signals derivable fromlocations inside or outside a human or animal body or body cavity,comprising the steps of obtaining samples of said signals at specificintervals, and converting thus sampled pressure signals intopressure-related digital data with a time reference, wherein forselectable time sequence windows the method comprising the further stepsof:

-   a) identifying from said digital data single pressure waves related    to cardiac beat-induced pressure waves,-   b) identifying from said digital data pressure waves related to    artifacts or a combination of artifacts and cardiac beat-induced    pressure waves,-   c) computing single pressure wave (SW.x)-related parameters during    individual of said time sequence windows,-   d) computing delta single pressure wave (ΔSW.x)-related parameters    between subsequent single pressure waves (n−1;n) within said time    sequence windows,    -   said subsequent single pressure waves (n−1;n) representing a        current single pressure wave SW[n].x in time n subtracted from        the previous SW[n−1].x in time n−1 of said individual time        sequence window,-   e) computing time sequence (TS.x)-related parameters of said single    pressure waves during individual of said time sequence windows,-   f) computing delta time sequence (ΔTS.x)-related parameters between    subsequent time sequence windows (n−1;n) of said individual time    sequence windows,    -   said subsequent time sequence windows (n−1;n) representing a        current time sequence window TS[n].x in time n subtracted from        the previous TS[n−1].x in time n−1 of said individual time        sequence window,-   g) determining criteria for thresholds and ranges of said single    pressure wave (SW.x)-related parameters of said single pressure    waves during said time sequence windows,-   h) determining criteria for thresholds and ranges of said delta    single pressure wave (ΔSW.x)-related parameters between subsequent    of said single pressure waves during said time sequence windows,-   i) determining criteria for thresholds and ranges of said time    sequence (TS.x)-related parameters of said single pressure waves    during said time sequence windows,-   j) determining criteria for thresholds and ranges of said delta time    sequence (ΔTS.x)-related parameters between subsequent time sequence    windows, and-   k) using said criteria for thresholds and ranges in steps g)-j) to    provide optimal differentiating between single pressure waves caused    by cardiac beat-induced pressure waves and pressure waves caused by    artifact-induced pressure waves or a combination thereof.

Further embodiments of this first aspect of the invention are derivablefrom the descriptive portion of the specification and the relateddrawings.

This first feature of the invention should be considered an iterativeprocess aiming at establishing a process method for optimalidentification of pressure waves created by cardiac beat-inducedpressure waves. Said criteria can be determined for specific signals inseveral recordings (each recording containing one or more signals).After determining said criteria in a selectable number of signals, thesenew criteria are determined and implemented in the inventive method.Both manual and automatic verification may be used to evaluate whetherthe improved method identifies pressure waves created by cardiacbeat-induced pressure waves, not pressure waves created by artifacts ora combination thereof. Different criteria are determined for differenttypes of signals, different locations of signals, and different sensorswherefrom signals are derived.

The significance of determining criteria related to thresholds andranges of said single wave (SW.x)-, delta single wave (ΔSW.x)-, timesequence (TS.x)- and delta time sequence (ΔTS.x)-related parameters isthat a precise tool is created for excluding pressure waves not relatedto cardiac beat-induced pressure waves. It has been shown that forspecific types of signals and locations, said criteria are quite narrowfor single pressure waves related to cardiac beat-induced pressurewaves. This fact makes it easy to identify artifact-induced pressurewaves, and it is very useful when comparing identical pressuresderivable from different locations. Thus, a very precise tool foridentification of pressure waves related to cardiac beat-inducedpressure waves is established, which is crucial for other features ofthis invention. Such differentiation between single pressure wavescreated by cardiac beat-induced pressure waves, pressure waves caused byartifacts, or a combination of cardiac beat and artifact-inducedpressure waves is not possible by currently used, prior art technology.

Current and prior art technology of processing pressure signals giveslimited and less useful information about the pressures that aremeasured. An example is given. The major reason for continuousintracranial pressure (ICP) monitoring is to obtain information aboutcerebral elastance and compliance. Elastance (E) is a function of therelationship between pressure change and volume change (E=ΔP/ΔV).Increased cerebral elastance (E) implies that a small intracranialvolume increase (ΔV) causes a large intracranial pressure increase (ΔP).Compliance (C) is the inverse of elastance (C=1/E). These relationshipshave been described in the pressure-volume curve of Langfit, which iswell known from the prior art. Current, prior art technology ofprocessing continuous intracranial pressure (ICP) signals gives no orminimal information about compliance. Instead rather complexpressure-volume relationships must be computed based on introducedvolume changes during continuous intracranial pressure (ICP) monitoring.Another major challenge with current technology is that pressuremeasurements are very sensitive to calibration against a zero pressurelevel. Pressure signals derived from inside a human or animal body orbody cavity usually are absolute pressure values, which are relative toatmospheric pressure level. Problems related to calibration against saidatmospheric pressure level or related to drift of a zero pressure levelduring a measurement may give misleading pressure measurements.

In a second feature of this invention a method for processing continuouspressure-related signals is described that extracts new informationabout pressures from the pressure signal itself. More specifically saidsecond feature of the invention relates to: A method for processingcontinuous pressure-related signals derivable from locations inside oroutside a human or animal body or body cavity, comprising the steps ofobtaining samples of said signals at specific intervals, and convertingthus sampled pressure signals into pressure-related digital data with atime reference, wherein for selectable time sequence windows the methodcomprising the further steps of:

-   a) identifying from said digital data single pressure waves related    to cardiac beat-induced pressure waves,-   b) identifying from said digital data pressure waves related to    artifacts or a combination of artifacts and cardiac beat-induced    pressure waves,-   c) computing time sequence (TS.x)-related parameters of said single    pressure waves during individual of said time sequence windows, and-   d) establishing an analysis output selected from one or more of said    time sequence (TS.x)-related parameters of said single pressure    waves during individual of said time sequence windows:    -   d1) mean wave amplitude (TS.MeanWavedP),    -   d2) mean wave latency (TS.MeanWavedT),    -   d3) mean wave rise time coefficient (TS.MeanWaveRT),    -   d4) mean amplitude (TS.MeandP),    -   d5) mean latency (TS.MeandT),    -   d6) mean rise time coefficient (TS.MeanRT), and    -   d7) mean single wave pressure (TS.Mean_(SW)P).

Further embodiments of this second feature of the invention arederivable from the descriptive portion of the specification and therelated drawings.

The analysis output of said method may be presented in a variety of wayssuch as numerical values; trend plots of numerical values, histogrampresentations or as a quantitative matrix. Thereby completely newinformation about pressures is obtained.

The significance of said analysis output is great. For example,computation of TS.MeanWavedP or TS.MeandP makes it possible todemonstrate reduced intracranial compliance, not revealed by current,prior art technology. Thus, information about cerebral compliance isderived from the intracranial pressure (ICP) signal itself. Thedetermined values of these parameters are highly predictable of responseto extra-cranial shunt treatment and selection of shunt valve opening.Determination of these parameters (e.g. TS.Mean_(SW)P) has made itpossible to determine whether a pressure signal is of good quality or ofbad quality. For example, in test measurements of several patients (bothchildren and adults) it was found that intracranial pressure (ICP)values indicated normal pressures, despite subcutaneous (below skin)placement of a solid pressure sensor (Codman ICP Microsensor, Johnson &Johnson, Raynham, Mass.). Mean ICP was computed according to existingand prior art technology, as sum of pressure levels divided by number ofsamples independent whether pressure waves were related to cardiac beatsor artifacts. In these cases current and prior art technology computedwrong and misleading pressure measurements. Determination ofTS.Mean_(SW)P (or other of said TS.x-related parameters) according tothis invention, revealed the bad signal quality since no single pressurewaves were identified, and hence no time sequence (TS.x) parameters werecomputed. Furthermore, problems related to zero calibration againstatmospheric pressure are eliminated by said second feature of thisinvention. This is related to the fact that TS parameters such asd1)-d7) all are relative values, not influenced by said atmosphericpressure level.

Current and existing technology includes a number of approaches andmethods for non-invasive pressure monitoring, i.e. measuring pressuresinside a body or body cavity without introducing the sensor inside saidbody or body cavity. For example, different variations of applanationtechnology may be used for measuring arterial blood pressure, ocularpressure (and intracranial pressure in infants). Pressure-relatedsignals indicative of intracranial pressure (ICP) may be derived fromtranscranial Doppler signals, cranial impedance-related signals orpressure-related signals from within the outer ear channel afterairtight sealing of the outer ear channel. However, a problem with saidmethods of non-invasive pressure monitoring is that only relativechanges in pressure over time may be measured, related to the fact thatthe absolute pressures within the body cavity remain unknown duringnon-invasive pressure measurements. Another problem relates to the factthat current and existing technology of non-invasive pressure monitoringprovides for no or minimal quality control whether the measured signalsare good or bad. This is at least partly related to lack of a referencematerial of what is considered as good signals and bad signals.

A third feature of this invention, as claimed in attached claim 1,provides for a method of comparing signals derived simultaneously fromdifferent locations. Thereby relationships between such simultaneoussignals may be determined. A reference material can be built up forrelationships between specific signals (and attributes such as locationand sensor type). These established relationships may subsequently beused for formula-based adjustments of individual signals solely obtainedby a non-invasive approach, thus modifying the individual continuousnon-invasive pressure-related signals into signals highly predictable ofthe corresponding invasive pressure-related signals. More specificallysaid third feature of the invention relates to: A method for processingtwo or more simultaneous continuous pressure-related signals derivablefrom a human or animal body from one or more locations thereof electablefrom: inside the body, outside the body, inside body cavity, outsidebody cavity, comprising the steps of obtaining samples of said signalsat specific intervals, and converting thus sampled pressure signals intopressure-related digital data with identical time reference, wherein forselectable and simultaneous time sequence windows the method comprisingthe further steps of:

-   a) identifying from said digital data single pressure waves related    to cardiac beat-induced pressure waves within said two or more    simultaneous signals constituting a pressure recording,-   b) identifying from said digital data pressure waves related to    artifacts or a combination of artifacts and cardiac beat-induced    pressure waves within said two or more simultaneous signals    constituting a pressure recording,-   c) computing time sequence (TS.x)-related parameters of said single    pressure waves during said identical time sequence windows within    said two or more simultaneous signals constituting a pressure    recording,    wherein the method comprising the further steps of:-   d) determining relationships between time sequence (TS.x)-related    parameters of said identical time sequence windows within said two    or more simultaneous signals constituting a pressure recording,    -   said relationships calculated as related time sequence (rTS.x)        parameters, and-   e) determining said related time sequence (rTS.x) parameters for a    selectable number of recordings,    -   said related time sequence (rTS.x) parameters to be used for        formula-based adjustment of time sequence (TS.x)-related        parameters of individual pressure-related signals.

Further embodiments of this third feature of the invention are definedin sub-claims 2-25. These embodiments are also derivable from thedescriptive portion of the specification and the related drawings.

Preferably said related time sequence (rTS.x) parameters should beobtained for a population of recordings in order to determine differentformula-based relationships for specific types of signals and locationsand sensor types. This procedure subsequently creates a referencematerial that may be applied on new and individual non-invasive pressuremeasurements. Thereby continuous pressure-related signals from anon-invasive source may be processed in a way that makes the continuousnon-invasive pressure signals highly predictable of the pressures insidethe body or body cavity.

It has been found that the significance of said third feature of theinvention is great, as related to comparisons of identical pressuresfrom different locations. By using the method described according to thethird feature of the invention, continuous pressure signals derived froman epidural location became highly predictable of continuous pressuresignals derived from within the brain parenchyma, i.e. intra-dural.Thereby, the invention enables epidural intracranial pressure (ICP) tobe as precise as intra-dural pressure measurements from inside the brainparenchyma. This is a major advantage as epidural pressure measurementsare less invasive, not requiring penetration of a cannula or sensor intothe brain parenchyma. Furthermore, continuous pressure-related signalsderived from the outer ear channel became highly predictable ofcontinuous pressure signals derived from within the brain parenchymaitself. Thus, this third feature of the invention gives a technicalsolution to a major problem of non-invasive pressure recording, namelythat absolute pressure relative to atmospheric pressure inside the bodyor body cavity is unknown during non-invasive pressure recording.

A technical problem related to measuring air pressure within the outerear channel is to obtain airtight sealing of the outer ear channel. Thismay be practically difficult as the ear channel is very different fromindividual to individual.

According to a fourth feature of this invention, a technical solution isgiven to the problem of airtight sealing of an outer ear channel. Adevice for closing of said outer ear channel is described. Morespecifically said fourth feature of the invention relates to: A devicefor use in sensing continuous pressure-related signals throughnon-invasive pressure measurements on a human or animal body, comprising

-   -   a pressure sensor with a pressure sensing tube, said tube        insertable into a human or animal outer ear channel spaced from        a tympanic membrane thereof,    -   wherein inflatable means surrounds an outside length of the        tube, said inflatable means upon inflation thereof sealingly        closing an annular gap between a region of said tube and a wall        region of said outer ear channel.

The significance of this device is that said inflatable means isthin-walled and soft, thus making airtight sealing of an outer earchannel possible, independent on the diameter of the outer ear channel.Thereby one size of said sensor may be used independent of the diameterof the outer ear channel.

Further embodiments of this fourth feature of the invention arederivable from the descriptive portion of the specification and therelated drawings.

Drainage of fluid from a human brain or spinal fluid cavity can berequired in the case of excess fluid within one of said cavities. Fluidflow valves developed for this kind of fluid drainage was introduced tothe marked in the 1950's, and later on valves with adjustable fluid flowrate were introduced. Even after about half a century with using suchvalves for drainage of cerebrospinal fluid (CSF), there are stillunsolved technical problems related to such valves: Over-drainage ofcerebrospinal fluid (CSF) represents a great and unsolved problem withall kinds of shunts. No shunts allow for adjustment of shunt valveopening and fluid flow rate based on pressure measurements withdetermination of cerebral compliance.

According to a fifth feature of this invention, technical problemsrelated to current and prior art technology of draining excess fluid aresolved. More specifically said fifth feature of the invention relatesto: A device for use in draining excess fluid from a brain or spinalfluid cavity unto another body cavity of a human being, comprising:

-   -   a first drainage tube having an inlet thereof located in said        brain or spinal fluid cavity, said first drainage tube connected        to the inlet of a fluid flow controllable valve,    -   a valve-opening regulator with associated control unit being        connected to a regulator and processing unit, the control output        from which is a function of pressure-sensing signals derived        from at least one pressure sensor, a pressure transducer        transforming said pressure-sensing signals into signals        processed by said processing unit, a power supply, information        transferable means, and    -   a second drainage tube from an outlet of said valve opening        having a distal outlet thereof, said distal outlet opening into        said another human body cavity,        wherein    -   said first drainage tube, said fluid flow controllable valve,        said valve-opening regulator, said control unit, said regulator,        said processing unit, said pressure transducer, said power        supply, said information transferable means and said second        drainage tube being located below a skin surface of said human        body.

Further embodiments of this fifth feature of the invention are derivablefrom the descriptive portion of the specification and the relateddrawings.

The significance of the new inventive feature of this device relates toincorporation of said processing unit, which enables accurate control ofvalve opening and fluid flow rate. Said control is based on output ofanalyzing pressure-related signal from cavity wherein fluid is drained.Shunt valve opening and fluid flow rate may be regulated in aphysiological way, i.e. the fluid flow rate is regulated according tointracranial compliance. Drainage of fluid is regulated to obtainoptimal cerebral compliance. This inventive aspect represents atechnical solution to the problem of over-drainage. In addition, in thecase of suspected shunt failure, information about cerebral compliancecan be obtained using said information transferable means.

According to a sixth feature of this invention a method is described forprocessing continuous pressure-related signals, said method beingincorporated in said processing unit of said device for use in drainingexcess fluid from a brain or spinal fluid cavity. More specifically saidsixth feature of the invention relates to: A method for processingcontinuous pressure-related signals derivable from locations inside oroutside a human body or body cavity, comprising the steps of obtainingsamples of said signals at specific intervals, and converting thussampled pressure signals into pressure-related digital data with a timereference, wherein for selectable time sequence windows the methodcomprises the further steps of:

-   a) identifying from said digital data single pressure waves related    to cardiac beat-induced pressure waves,-   b) identifying from said digital data pressure waves related to    artifacts or a combination of artifacts and cardiac beat-induced    pressure waves,-   c) computing time sequence (TS.x)-related parameters of said single    pressure waves during individual of said time sequence windows,-   d) establishing an analysis output of said time sequence    (TS.x)-related parameters for a selectable number of said time    sequence windows,-   e) establishing a deliverable first control signal related to an    analysis output in step d) for a selectable number of said time    sequence windows, said first control signal being determined    according to one or more selectable criteria for said analysis    output, and-   f) modifying said deliverable first control signal into a second    control signal to provide a performance modifying signal.

Further embodiments of this sixth feature of the invention are derivablefrom the descriptive portion of the specification and the relateddrawings.

The significance of this method is related to the fact thatphysiological regulation of shunt valve opening and valve fluid flowrate is found. It has been found that an analysis output related toTS.MeanWavedP (or other parameters such as e.g. TS.MeanWavedT,TS.MeandP, and TS.MeandT) was very useful for adjustment of shunt valveopening. The inventive method enables regulation of a shunt valve in away that is not available by current, prior art technology. Furthermore,in case of suspected shunt dysfunction, computation of said TS.xparameters provides new information whether suspected shunt malfunctionincludes over- or under-drainage.

The identifying and computing steps of said process method related tothe sixth feature of the invention are comparable to the identifying andcomputing steps of the process method related to the second feature ofthe invention.

Said methods of said first, second, third, and sixth features of thisinvention relate to methods for processing continuous pressure-relatedsignals independent on locations of said continuous pressure-relatedsignals. Examples of human or animal body cavities include: intracranialcavity (independent of whether intra- or extra-ventricular, intra-duralor epidural, spinal or cranial), ocular cavity, inner or outer earcavities, intra-arterial cavity (independent of whether intra-arterialor intra-cardiac), intra-venous cavities (independent of wherein thevarious venous cavities), gastro-intestinal body cavities (independentof the specific type of gastrointestinal cavity such as esophagealpressure, duodenal pressure, intraperitoneal pressure), and urinarytract pressure (independent of which part of the urinary tract cavity).Said methods for processing continuous pressure-related signals are aswell independent on type of pressure sensor, thus independent on whethersaid sensors are placed inside or outside a human or animal body or bodycavity.

According to a seventh feature of this invention a system is describedfor processing continuous pressure-related signals, said system beingused for controlling drainage of excess fluid from a first body cavityto a second body cavity of a human being. More specifically said seventhfeature of the invention relates to: A system for processing continuouspressure-related signals derivable from one or more sensor(s) havinglocation(s) inside or outside a body or body cavity of a human being,said system comprising:

-   a) means for on basis of said signals receivable from said sensor(s)    via pressure transducer means to control drainage fluid flow rate    from a first body cavity to a second body cavity in one said human,-   b) a processing device in said system having means for processing    said signals, said processing means including sampling means for    sampling said signals at specific intervals,-   c) converter means for converting the sampled signals into pressure    related digital data with a time reference,-   d) means for during selectable time sequence windows identifying    from said digital data single pressure waves related to cardiac    beat-induced pressure waves, and related to artifacts or a    combination of cardiac beat-induced waves and artifacts,-   e) means for computing and analyzing said digital data during said    selectable time sequence windows,-   f) means for outputting to device terminal means one or more    pressure parameter signals related to a selectable number of said    time sequence windows:    -   f1) mean wave amplitude (TS.MeanWavedP),    -   f2) mean wave latency (TS.MeanWavedT),    -   f3) mean wave rise time coefficient (TS.MeanWaveRT),    -   f4) mean amplitude (TS.MeandP),    -   f5) mean latency (TS.MeandT),    -   f6) mean rise time coefficient (TS.MeanRT),    -   f7) mean single wave pressure (TS.Mean_(SW)P), and-   g) a valve device controlling the drainage fluid flow rate and    connectable to said body cavities,-   h) regulator unit means connectable to said terminal means for    receiving at least one of said parameter signals, said regulator    unit means being capable of establishing a device performance    modifying signal by means of one of said pressure parameter signals    or a combination effect obtained from using at least two of said    pressure parameter signals, wherein said performance modifying    signal deliverable from said regulator unit being capable of    controlling said drainage fluid flow rate through said valve device    by input to a valve-opening regulator.

Further embodiments of this seventh feature of the invention arederivable from the descriptive portion of the specification and therelated drawings.

There are several major advantages with this seventh feature of theinvention, as compare to current, prior art technology. The systemenables assessment of cerebral compliance that may be used to controldrainage of excess fluid, thereby ensuring optimal cerebral compliance.Furthermore, the system provides for quality control of the continuouspressure-related signals, thus reducing the risk of computing false ormisleading pressure parameters.

According to an eight feature of this invention a device is describedfor use in sensing continuous pressure-related signals. Said device canbe used to display new information from pressure measurements derivedfrom the inventive method of processing continuous pressure-relatedsignals. More specifically said eight feature of the invention relatesto: A device for use in sensing continuous pressure-related signalsderivable from locations inside or outside a human or animal body orbody cavity, comprising

-   -   a pressure sensor with a pressure sensing element,    -   a pressure transducer capable of transforming said        pressure-related signals into digital pressure-related signals,    -   a processing unit with input means for receiving said        pressure-related digital signals, said processing unit providing        at output means thereof one or more of the following time        sequence parameters during selectable time sequence windows of        said pressure-related signals:        -   mean wave amplitude (TS.MeanWavedP),        -   mean wave latency (TS.MeanWavedT),        -   mean wave rise time coefficient (TS.MeanWaveRT),        -   mean amplitude (TS.MeandP),        -   mean latency (TS.MeandT),        -   mean rise time coefficient (TS.MeanRT),        -   mean single wave pressure (TS.Mean_(SW)P),    -   a display unit connected to said output means for selectively        displaying said one or more parameters, and    -   means for supplying power to power consuming parts of the        device.

Further embodiments of this eight feature of the invention are derivablefrom the descriptive portion of the specification and the relateddrawings.

The significance of this eight feature of the invention is important ascompared to existent and prior art technology. The inventive method ofprocessing continuous pressure-related signals providing for completelynew information about pressures can be incorporated in this device.Various modifications of the device are within the scope of theinvention. Thereby new information about pressures can be obtained invarious settings such as within the home of a patient or within thehospital. Said new information can be derived from the continuouspressure signal itself and be displayed, enabling quick diagnosis andintervention.

According to the ninth feature of this invention a device is describedfor use in sensing continuous pressure-related signals derivable fromlocations inside or outside a human or animal body or body cavity. Thisdevice is a sensor device or a combined sensor-display device. Theinventive method of processing continuous pressure-related signals is apart of said device. More specifically said ninth feature of theinvention relates to: A device for use in sensing continuouspressure-related signals derivable from locations inside or outside ahuman or animal body or body cavity, comprising

-   -   a pressure sensor with a pressure sensing element,    -   a pressure transducer capable of transforming said        pressure-related signals into digital pressure-related signals,    -   a processing unit with input means for receiving said        pressure-related digital signals, said processing unit providing        at output means thereof one or more of the following time        sequence parameters during selectable time sequence windows of        said pressure-related signals:        -   mean wave amplitude (TS.MeanWavedP),        -   mean wave latency (TS.MeanWavedT),        -   mean wave rise time coefficient (TS.MeanWaveRT),        -   mean amplitude (TS.MeandP),        -   mean latency (TS.MeandT),        -   mean rise time coefficient (TS.MeanRT),        -   mean single wave pressure (TS.Mean_(SW)P),    -   information transfer means connected to said output means and        enabling transferal of information to an external unit of at        least said one or more parameters, and    -   means for supplying power to power consuming parts within the        device.

Further embodiments of this ninth feature of the invention are derivablefrom the descriptive portion of the specification and the relateddrawings.

There are several major advantages with said ninth feature of theinvention. By means of the sensor device itself completely newinformation from pressure measurements can be derived from a sensordevice itself. Modifications of said sensor device are within the scopeof the invention. One possible modification of said sensor device can beimplanted within the patient. For example, said sensor device can beimplanted within a human or animal body or body cavity, enablingpressure measurements at any time, using information transfer means ofsaid sensor device. As compared to current and prior art technology,this sensor device provides completely new information about pressures.This new information is not dependent on a zero pressure level againstatmospheric pressure, which is crucial for implantable modifications ofsaid sensor device.

The tenth feature of this invention relates to a system for processingcontinuous pressure-related signals derivable from one or more sensor(s)having location(s) inside or outside a body or body cavity of a humanbeing or animal. This system can be incorporated in various devices. Thesystem incorporates the inventive method of processing continuouspressure-related signals. More specifically said tenth feature of theinvention relates to: A system for processing continuouspressure-related signals derivable from one or more sensor(s) havinglocation(s) inside or outside a body or body cavity of a human being oranimal, said system comprising:

-   means for on basis of said signals receivable from said sensor(s)    via pressure transducer means to display output of said processing,    and-   a processing unit in said system having means for processing said    signals, said processing means including:-   a) sampling means for sampling said signals receivable from said    pressure transducer means at specific intervals,-   b) converter means for converting the sampled signals received from    said sampling means into pressure related digital data with a time    reference,-   c) identifying means for during selectable time sequence windows    identifying from said digital data output from said converter means    single pressure waves related to cardiac beat-induced pressure    waves, related to artifacts, or a combination of cardiac    beat-induced waves and artifacts,-   d) computing means for computing time sequence parameters from    included or selected time sequence windows output from said    identifying means,-   e) analyzing means for analyzing said time sequence parameters in    the form of digital data related to said selectable time sequence    windows,-   f) output means for outputting to device terminal means one or more    pressure parameters related to a selectable number of said time    sequence windows:    -   f1) mean wave amplitude (TS.MeanWavedP),    -   f2) mean wave latency (TS.MeanWavedT),    -   f3) mean wave rise time coefficient (TS.MeanWaveRT),    -   f4) mean amplitude (TS.MeandP),    -   f5) mean latency (TS.MeandT),    -   f6) mean rise time coefficient (TS.MeanRT),    -   f7) mean single wave pressure (TS.Mean_(SW)P),        and-   g) means for supplying power to power consuming parts within the    system.

Further embodiments of this tenth feature of the invention are derivablefrom the descriptive portion of the specification and the relateddrawings.

The important aspect of this tenth feature of the invention is that theinventive method can be incorporated within said system. Said system canfurther be incorporated in many devices, providing for new diagnosticinformation, not available by current and prior art technology.

The invention is now to be further described with reference toadvantageous, exemplifying embodiments and alternatives thereof, as alsosupported by the attached drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an overview of notations related to important terms of thisinvention.

FIG. 2 shows a flowchart over a method for processing continuouspressure-related signals including determination of criteria forthresholds and ranges of single wave (SW.x)-, delta single wave(ΔSW.x)-, time sequence (TS.x)- and delta time sequence (ΔTS.x)-relatedparameters.

FIG. 3 a shows two subsequent individual time sequence windows includingall identified peaks and valleys related to both cardiac beat-inducedsingle pressure waves and artifact-induced pressure waves or acombination thereof, FIG. 3 b shows pressure waves with all detectedpeaks and valleys, and FIG. 3 c shows single pressure waves withincluded pair combinations of peaks and valleys.

FIG. 4 a shows one time sequence window including all identified peaksand valleys, and FIG. 4 b shows the same time sequence window includingonly the accepted valley-peak pairs whereby the single pressure wave(SW.x)-related parameters are identified.

FIG. 5 a shows one individual single pressure wave including singlepressure wave (SW.x)-related parameters, and FIG. 5 b shows twosubsequent single pressure waves (n−1;n) including single pressure wave(SW.x)- and delta single wave (ΔSW.x)-related parameters.

FIG. 6 shows one individual time sequence window including varioustime-sequences (TS.x)-related parameters.

FIG. 7 a shows two subsequent time sequence windows of a continuouspressure signal derived from a sensor within the brain parenchyma(Signal [1]), and FIG. 7 b shows two subsequent time sequence windows ofa continuous pressure signal derived from a sensor within theintracranial epidural space (Signal[2]).

FIG. 8 shows a flow chart over a method for processing continuouspressure-related signals including determination of time sequence(TS.x)-related parameters.

FIG. 9 shows a flow chart over a method for processing two simultaneouscontinuous pressure-related signals including determination of relatedtime sequence (rTS.x) parameters, which are used for formula-basedadjustments of individual signals.

FIG. 10 a shows a schematic drawing of a sensor detecting air pressurewithin the outer ear channel after airtight sealing by an inflatableballoon, FIG. 10 b shows one individual time sequence of a continuouspressure-related signal derived from the sensor placed within the brainparenchyma (Signal[1]), FIG. 10 c shows the identical time sequence froma continuous pressure related signals derived from a sensor measuringair pressure within the closed outer air channel (Signal[2]), and FIG.10 d shows trend plots of TS.MeandP of Signal[1] ad fTS.MeandP ofSignal[2].

FIG. 11 a shows trend plots of TS.MeandP of a continuouspressure-related signal derived from within the brain parenchyma andTS.MeandP of a continuous pressure-related signal derived from the outerear channel after airtight sealing of the outer ear channel, and FIG. 11b shows trend plots of TS.MeandP and fTS.MeandP.

FIG. 12 a shows a schematic presentation of a device connecting a firstand a second body cavity including a valve for draining excess fluidfrom a first body cavity, and FIG. 12 b shows a putative relationshipbetween valve opening and fluid flow rate of a fluid flow ratecontrollable valve.

FIG. 13 a shows changes in absolute mean intracranial pressure(TS.MeanP) during three measurement periods including three adjustmentsof a fluid flow rate controllable valve, FIG. 13 b shows changes in meanintracranial pressure wave amplitude (TS.MeanWavedP) during threemeasurement periods including three adjustments of a fluid flow ratecontrollable valve, and FIG. 13 c shows changes in mean intracranialpressure wave latency (TS.MeanWavedT) during three measurement periodsincluding three adjustments of a fluid flow rate controllable valve.

FIG. 14 shows a flow chart over a system for processing continuouspressure-related signals, used for control of drainage fluid flow ratefrom a first to a second body cavity.

FIG. 15 a is a schematic presentation of a sensor and display devicewith a sensor incorporated within the device, FIG. 15 b is a schematicpresentation of a display device with a pressure sensor connectable tothe device, FIG. 15 c is a schematic presentation of a sensor anddisplay device including information transfer means, parameter selectioncontrol means, parameter data storage means and with a sensorincorporated within the device, and FIG. 15 d is a schematicpresentation of a display device including information transfer means,parameter selection control means, parameter data storage means,however, with a pressure sensor connectable to the device.

FIG. 16 a is a schematic presentation of a sensor device with a pressuresensor incorporated within the device, FIG. 16 b is a schematicpresentation of a sensor related device with a sensor connectable to thedevice, FIG. 16 c is a schematic presentation of a sensor deviceincluding a display and with a sensor incorporated within the device,and FIG. 15 d is a schematic presentation of a device including adisplay, however, with a pressure sensor connectable to the device.

FIG. 17 shows a system for processing continuous pressure-relatedsignals, used in sensing continuous pressure-related signals, andincorporated in sensor and/or display devices.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

In FIG. 1 is shown an overview of notation and relationships related tocontinuous pressure signals, as used according to this invention. Arecording 101 is one or more simultaneous signals 102 derivable fromlocations inside or outside a human or animal body or body cavity, eachof said signals 102 having identical time reference, though it is not arequirement that start time 103 is identical for all signals of arecording. Each signal has the following attributes: type 104, frequency105, and actual samples 106. Given these values, end time 107 can becalculated. According to this invention a specific sampling frequency105, is not given. For single pressure wave analysis, it has been foundthat sampling frequencies of 100 to 200 Hz are useful. The notationList[x] is a reference to an element within an ordered list. Withreference to FIG. 1, some examples are given:

The notation Recording [l].Signal [m].Time Sequence [o].Single Wave [q])denotes a specific Single Wave [q] 108 within a specific Time Sequence[o] 109 within a specific Signal [n] 102, within a specific Recording[l] 101.

The notation Recording [l].Signal [m].Samples [n] denotes a specificSample [n] 106 within a specific Signal [m] 102 within a specificRecording [l] 101.

Signal.Samples[n] denotes a specific sample within a non-specificsignal, a general notation for a signal without specifying a dedicatedsignal.

The notation Recording [l].Signal [m].Delta Time Sequence [p] denotes aspecific Delta Time Sequence [p] 110 within a specific Signal [m] 102within a specific Recording [l] 101. The notation Recording [l].Signal[m].Time Sequence [o].Delta Single Wave [r] denotes a specific DeltaSingle Wave [r] 111 within a specific Time Sequence [o] 109 within aspecific Signal [m] 102 within a specific Recording [l] 101.

The ordering is always starting with one, e.g. the first available timesequence window within the first available signal within a recording isdenoted by Signal[1].Time Sequence[1].

Abbreviations may be used for Single Wave [m] (=SW[m]), Delta SingleWave [r] (=ΔSW[r]), Time Sequence [o] (=TS[o]) and Delta Time Sequence[p] (=ΔTS[p]).

It is important to note that attributes to type 104 are sensor 112 andlocation 113. Both these attributes are required to give a precisedescription of pressure signal type 104.

The notation Recording [l].Signal [m].Type.Location denotes a specificLocation 113 within a specific Type 104 within a specific Signal [n]102, within a specific Recording [l] 101. The notation Recording[l].Signal [m].Type.Sensor denotes a specific Sensor 112 within aspecific Type 104 within a specific Signal [n] 102, within a specificRecording [l] 101. The present invention relates to a method of alltypes of human or animal continuous pressure-related signals 102.

The inventive method for processing continuous pressure-related signals102 is independent on said locations 113, said locations being startingpoints (or origins) of said continuous pressure-related signals 102.Examples of said locations described in this document are given:Epidural refers to a location outside the dura mater of the brain butwithin the cranial vault. Intracranial/intra-dural refers to inside thedura mater of the brain. Spinal intra-dural refers to inside the duramater of the spinal cord. Intra-arterial refers to a location inside anarterial blood vessel.

Said method for processing continuous pressure-related signals 102 isindependent on type of pressure sensor 112, said sensor 112 being placedinside or outside a human or animal body or body cavity. There are manytypes of pressure sensors. A pressure sensor 112 can be a pressuremeasuring unit or pressure sensing element connected to a pressureconveying probe, e.g. a probe in the form of a fluid catheter and/orcannula. It should be understood that if a pressure sensor 112 isassociated with a fluid cavity, e.g. in the brain, the pressure sensor112 would conveniently be a element communicating with the cavity viathe probe, unless the unit is so small in size that it can be safelylocated within the fluid cavity, e.g. at the proximal tip of a probe orcatheter. A pressure sensor 112 can also be placed directly within thebody or body cavity. Three examples of pressure sensors are listed: TheBaxter (Truwave PX-600F vein/arterial anesthesia pressure monitoringkit) Health Care Corporation type of sensor measures fluid pressurewithin a catheter introduced via a cannula into a body cavity (e.g.cerebrospinal fluid body cavity, arterial blood cavity or venous bloodcavity). Codman ICP MicroSensor™ (Johnson & Johnson, Raynham, Mass.,USA) is a solid sensor which can be introduced into the brain parenchymafor intracranial pressure (ICP) monitoring. Spiegelberg ICP Probe 3XL(Spiegelberg, Aesculap, Germany) is a catheter which can be introducedinto a brain cerebrospinal fluid (CSF) cavity wherein the pressuresensing element is outside the patient, thus measuring pressure withinsaid Probe 3XL. These sensors are listed to exemplify that measurementof one pressure type (i.e. intracranial pressure) involves differentsensor types, and different locations of the sensor elements (i.e.outside or inside the intra-dural space).

A pressure signal 102 refers to a number of sequential and variablepressure samples 106 during a time period. A signal 102 containing acontinuous sequential number of samples 106 may be derived from a humanor animal body from one or more locations thereof electable from: insidethe body, outside the body, inside body cavity, outside body cavity.

As indicated in FIG. 1, a signal 102 is equivalent to: Recording[l].Signal [m].Samples [n].

A sample 106 is defined as: Pressure value at a specific time. Each ofsaid samples contains a pressure value at a specific time. A selectabletime sequence window is a selected time frame of said signal 102. Eachof said selectable time sequence windows is related to a number oftime-related sequential pressure samples 106, each sample 106 referencedby a sample number, and elapsed time that is determined by sample 106location number and sample frequency 105. A specific duration of saidtime sequence windows is not given. However, for test purposes there hasbeen used time sequence windows of 6 seconds duration. Said selectedtime frame lies in the range 5-15 seconds, though this represents nolimitation of the scope of the invention.

A major problem with current and prior art technology of processingcontinuous pressure-related signals is a lack of useful methods forverifying whether pressure signals are based on pressure waves createdby the cardiac beats, not resulting from artifact waves or a combinationof artifact waves and cardiac beat-induced pressure waves. The firstfeature of this invention is a method for best possible differentiationbetween pressure waves caused by either cardiac beat-induced pressurewaves, artifact-induced pressure waves or a combination of artifact- andcardiac beat-induced pressure waves. This first inventive feature is ofmajor significance since pressure measurements based on artifacts aremisleading, giving wrong diagnostic information. The significance iseven greater when continuous pressure-related signals are derived fromoutside as compared to inside a human or animal body or body cavity dueto a larger proportion of artifacts.

Reference is now given to the first feature of the invention. Variousaspects related to this first inventive feature are particularlyillustrated in FIGS. 2, 3, 4, 5, 6, and 7.

First, reference is given to FIG. 2, providing an overview of the methodfor processing continuous pressure-related signals 201 derivable fromlocations inside or outside a human or animal body or body cavity,corresponding to said first feature of the invention. More details aboutthe method are given together with detailed description of FIGS. 3, 4,5, 6 and 7. The method for processing continuous pressure-relatedsignals 201 derivable from locations inside or outside a human or animalbody or body cavity, comprises the steps of sampling said signals atspecific intervals, and converting thus sampled pressure signals intopressure-related digital data signal 201 with a time reference. Forselectable time sequence windows, the method comprises the further stepsof identifying from said digital data the single pressure waves relatedto cardiac beat-induced pressure waves, and the pressure waves relatedto artifacts or a combination of artifacts and cardiac beat-inducedpressure waves. The process of differentiating between these differenttypes of pressure waves is illustrated in FIG. 2. The process methodincorporates different Identifying Steps, Computing Step and DeterminingSteps.

Said Identifying Steps include identification 202 of all separate peaksand valleys in said sampled signal 201. Each of said peaks is a samplewith a pressure value and a time stamp or location, and each of saidvalleys is a sample with a pressure value and a time stamp or location.The result of applying General Methods Criteria 203 is either included,i.e. accepted, peak/valley pair combinations 204 or excluded, i.e.rejected, peak/valley pair combinations 205. After applying the SingleWave & Delta Single Wave Criteria 206 to said included peak/valley pairs204, the output is either included, i.e. accepted, single pressure waves207 or excluded, i.e. rejected, pressure waves 208. Said criteria 206relate to thresholds and ranges of single pressure wave (SW.x)-relatedparameters and delta single pressure wave (ΔSW.x)-related parametersduring time sequence windows. Included pair combinations 207 ofpeak/valley 204 pairs in said signal 201 correspond to accepted paircombinations of diastolic minimum pressure (SW.P_(min1)) and systolicmaximum pressure (SW.P_(max)), which characterize single pressure wavescreated by cardiac beat-induced pressure waves. Said criteria 206exclude minimum-maximum pressure (SW.P_(min1)/SW.P_(max)) pairs withsaid single pressure wave (SW.x)- and delta single pressure wave(ΔSW.x)-related parameters outside selectable thresholds and ranges.Pair combinations of diastolic minimum pressure (SW.P_(min1)) andsystolic maximum pressure (SW.P_(max)) correspond to the diastolicminimum pressures and systolic maximum pressures of individual ofpressure waves created by each of said cardiac beats. Time Sequence &Delta Time Sequence Criteria 209 are applied to each of said timesequence windows in a continuous series of said time sequence windowsduring a recording. Each time sequence window is a selected time frameof said signal. Said criteria 209 for thresholds and ranges of said timesequence (TS.x) and delta time sequence (ΔTS.x)-related parametersdetermine included, i.e. accepted, time sequence windows 210 andexcluded, i.e. rejected, time sequence windows 211, that are used forfurther analysis. Said criteria 209 exclude time sequence windows 211with said time sequence (TS.x)- and delta time sequence (ΔTS.x)-relatedparameters outside selectable thresholds and ranges.

At the Computing Step level, the following parameters are available:Single pressure wave (SW.x)-related parameters 212, delta singlepressure wave (ΔSW.x)-related parameters 213, and time sequence(TS.x)-related parameters 214 for each individual of included timesequence windows 210. In addition, delta time sequence (ΔTS.x)-relatedparameters 215 are computed between subsequent time sequences (n−1; n)of said individual included time sequence windows 210. Said subsequenttime sequence windows (n−1;n) represent a current time sequence window(TS[n].x) in time n subtracted from the previous TS[n−1].x in time n−1.

The Identifying Steps are applied to each of said time sequence windowsin a continuous series of said time sequence windows during a recording.

The Computing Step is applied to each of said included time sequencewindows 210 in a continuous series of said time sequence windows 210during a recording.

In subsequent Determining Steps, criteria 216 are determined forthresholds and ranges of said SW.x-, ΔSW.x-, TS.x- and ΔTS.x-relatedparameters. One major application 217 of said criteria is to providebest possible differentiating between single pressure waves caused bycardiac beat-induced pressure waves and pressure waves caused byartifact-induced pressure waves or a combination thereof. By changingthe set of criteria it is possible to change the proportion of excludedpressure waves and the proportion of excluded time sequence windows.There are several situations wherein the inventive step of determiningcriteria for optimal single pressure wave detection is crucial:

-   -   Determination of time sequence (TS.x)-related parameters derived        from an invasive signal can be based on a minimal influence of        artifact-induced pressure waves.    -   Determination of time sequence (TS.x)-related parameters derived        from a non-invasive signal can be based on a minimal influence        of artifact-induced pressure waves.    -   Determination of time sequence (TS.x)-related parameters derived        from an invasive signal within a processing unit regulating a        controllable shunt can be based on a minimal influence of        artifact-induced pressure waves.

A more detailed description of General Methods Criteria (FIG. 2) is nowgiven with reference to FIGS. 3 a, 3 b and 3 c. The process described inthis paragraph is intended to illustrate the concept, not to limit thescope of the invention. After the sampled pressure signal has beenconverted into a pressure-related digital data signal 301, all peaks 302and valleys 303 in said sampled signal 301 are identified. Each of saidpeaks is a sample with a pressure value and a time stamp or location,and each of said valleys is a sample with a pressure value and a timestamp or location. Each individual peak 302 and valley 303 is identifiedwith an absolute pressure value and a time stamp location.

First, it may be determined at which pressure levels the peaks 302 arelocated. The pressure level percentage lines 304 shown in FIG. 3 aillustrate that 70.3% of all peaks within the 12 seconds recordingperiod, have absolute pressure values between 20 and 35 mmHg, 22.2% ofall peaks 302 have absolute pressure levels between 35 and 50 mmHg, and7.5% of all peaks 302 have absolute pressure levels between 0 and 20mmHg. Systolic maximum pressures (SW.P_(max)) 305, corresponding tocardiac beat-induced systolic maximum pressures probably should be atthe absolute pressure level between 20 and 35 mmHg. A similar process isperformed for all identified valleys 303 during this time period of 12seconds. In the further analysis only those peaks 302 and valleys 303with absolute pressure values within the majority pressure range areconsidered.

Another step is further illustrated in FIGS. 3 b and 3 c whereby nearbypeaks 302 and valleys 303 are evaluated by using a so-called time window306. A time window 306 refers to a selected time period, e.g. of 0.10seconds duration. One of said General Methods Criteria (FIG. 2) definesthat only the peak 302 with greatest absolute pressure value should beselected when several peaks 302 are identified within said time window306.

Another of said General Methods Criteria (FIG. 2) define that only thevalley 303 with the lowest absolute pressure value should be selectedwhen several valleys 303 are identified within the same time window 306.In FIG. 3 b is illustrated all the identified peaks 302 and valleys 303within a short section of a continuous pressure-related signal 301.After application of the time window 306, it is shown in FIG. 3 c thatonly peaks 302 with greatest absolute pressure values and valleys 303with lowest absolute pressure values are remaining. In this example(FIG. 3 c), the peak 302 corresponds to the systolic maximum pressures(SW.P_(max)) 305, and the valley 303 corresponds to the startingdiastolic minimum pressure (SW.P_(min1)) 307. Thus, this process makespossible identification of included pair combinations of peaks 302 andvalleys 303 in said signal 301. Other criteria determine that therecannot be two pair combinations of SW.P_(max) 305 and SW.P_(min1) 307within one single wave duration (SW.WD), and that two different paircombinations of SW.P_(max) 305 and SW.P_(min1) 307 cannot contain eitheridentical peaks 302 or valleys 303. It should be understood that variousmodifications, additions and improvements of said General MethodsCriteria (FIG. 2) are within the scope of the invention.

The General Methods Criteria (FIG. 2) applied to continuous intracranialpressure (ICP) signals are shortly summarized:

-   -   Criteria_ICP_PeakValley_Y-WindowSize_MajorDistributionAreaPeaks:        -   a) Determine major distribution of peaks 302 to be used for            further analysis.    -   Criteria_ICP_PeakValley_Y-WindowSize_MajorDistributionAreaValleys:        -   a) Determine major distribution of valleys 303 to be used            for further analysis.    -   Criteria_ICP_PeakValley_X-WindowSize_GreatestPeakValue:        -   a) Determine the size of the window in which only one peak            302 with greatest absolute pressure value remains.    -   Criteria_ICP_PeakValley_X-WindowSize_LowestValleyValue:        -   a) Determine the size of the window in which only one valley            303 with lowest absolute pressure value remains.

It should be noted that the criteria listed here are shown to illustratethe use of said criteria, not to limit the scope of the invention. Newand other criteria can be developed for specific types of signals.

As indicated in FIG. 3 a, a continuous pressure-related signal 301involves two dimensions, indicated by a time scale 308 and a pressurescale 309. It is also indicated that all peaks 302 and valleys 303 insaid sampled signal 301 are identified during two consecutive timesequence windows, time sequence window no. one 310 and time sequencewindow no. two 311, each of 6 seconds duration. A time sequence windowis a selected time frame of said sampled signal. The duration of saidtime sequence windows 310, 311 is selectable. For test purposes therewere used durations of 6 seconds, though the exact duration of a timesequence represents no limitation of the invention. Preferably theduration of said selectable time sequence lies in the range 5-15seconds. Each of said selectable time sequence windows is related to anumber of time-related sequential pressure samples, each samplereferenced by a sample number and elapsed time determined by samplelocation number and sample frequency. The method is applied to thecontinuous pressure-related signal for each of said time sequences in acontinuous series of said time sequences during a continuous measurementperiod.

The notation Recording [l].Signals [m].Time sequence [o] denotes aspecific Time sequence [o] within a specific Signal [m] within aspecific Recording [l].

After determining included pair combinations of peaks 302 and valleys303 in said signal 301, said Single Wave & Delta Single Wave Criteria(FIG. 2) are used to determine whether said peak 302/valley 303 pairscorrespond to pair combinations of diastolic minimum pressure(SW.P_(min1)) 307 and systolic maximum pressure (SW.P_(max)) 305 thatcharacterize single pressure waves created by the cardiac beat-inducedpressure waves. All pair combinations of peaks 302 and valleys 303correspond to potential pair combinations of SW.StartP_(min1) 307 andSW.P_(max) 305.

For included, i.e. accepted, single pressure waves 312 said peak302/valley 303 pairs correspond to SW.P_(max) 305/SW.P_(min1) 307 pairsthat also correspond to the diastolic minimum pressures and systolicmaximum pressures of individual of pressure waves created by each ofsaid cardiac beats. Thereby, each included single pressure wave 312created by a cardiac beat-induced pressure wave is identified bysystolic maximum pressure (SW.P_(max)) 305 related to a cardiacbeat-induced systolic maximum pressure and a starting diastolic minimumpressure (SW.StartP_(min1)) 307 related to a cardiac beat-induceddiastolic minimum pressure. The ending diastolic minimum pressuredefines an end of a first single pressure wave (SW.P_(min2)) 313 whichmay be the same as the starting diastolic minimum pressure (SW.P_(min1))defining the start of the subsequent second single pressure wave. Ifthere is no subsequent second single pressure wave, the ending diastolicminimum pressure of a first single pressure wave (SW.P_(min2)) 313 isnot same as the starting diastolic minimum pressure (SW.P_(min1)) 307 ofanother single pressure wave.

Excluded, i.e. rejected, pressure waves 314 contain peak 302/valley 303pairs that do not meet the Single Wave & Delta Single Wave Criteria(FIG. 2). Said excluded pressure waves 314 are created by artifacts or acombination of cardiac beat- and artifact-induced pressure waves.

The Single Wave & Delta Single Wave Criteria (FIG. 2) applied to thecontinuous intracranial pressure (ICP) signal is shortly summarized:

-   -   Criteria_ICP_Intra-dural_SW.dP:        -   a) Amplitude (SW.dP) must be between 1.0 to 35.0 mmHg for            pair combinations of SW.P_(min1) 307 and SW.P_(max) 305 to            be included for further analysis.    -   Criteria_ICP_Intra-dural_SW.dT:        -   a) Latency (SW.dT) must be between 0.10 to 0.40 seconds for            pair combinations of SW.P_(min1) 307 and SW.P_(max) 305 to            be included for further analysis.

The specific criteria used in this example are not intended to limit thescope of the invention, but to illustrate application of said criteria.New and other criteria can be developed for specific types of signals,and criteria are determined for all single wave (SW.x)-relatedparameters and delta single pressure wave (ΔSW.x)-related parameters.

The Identifying Steps (FIG. 2) determining included single pressurewaves are further illustrated in FIGS. 4 a and 4 b. In FIG. 4 a is shownthe step of identifying all separate peaks and valleys, and in FIG. 4 bis shown the step of determining included single pressure waves on thebasis of included peak/valley pairs. In FIG. 4 a is shown a continuousintracranial pressure (ICP) signal 401 including pressure waves 402. Thetwo dimensions of the pressure signal are indicated, namely the pressurescale 403 and the time scale 404. Only one time sequence window 405 ofthe continuous pressure signal 401 is shown (Recording[46].Signal[1]TimeSequence[360]), including all twenty-two identified peaks 406(filled squares) and all seventeen identified valleys 407 (opencircles). Each of said peaks 406 is a sample with a pressure value and atime stamp or location, and each of said valleys 407 is a sample with apressure value and a time stamp or location.

Further application of the General Methods Criteria (FIG. 2) determineincluded pair combinations of peaks 406 and valleys 407 in said signal401.

Application of the Single Wave & Delta Single Wave Criteria (FIG. 2) tothese included peak 406/valley 407 pairs determine included singlepressure waves 408 and excluded single pressure waves 409 within saidtime sequence window 405 (Recording[46].Signal[1]TimeSequence[360]).

Only two sets of Single Wave & Delta Single Wave Criteria were appliedto the peak 406/valley 407 pairs shown in FIG. 4 a:

Amplitude (SW.dP) must be between 1.0 to 35.0 mmHg for peak 406/valley407 pairs to be included for further analysis(=Criteria_ICP_Intra-dural_SW.dP); and latency (SW.dT) must be between0.10 to 0.40 seconds for peak 406/valley 407 pairs to be included forfurther analysis (=Criteria_ICP_Intra-dural_SW.dT).

Included peak 406/valley 407 pairs correspond to included, i.e.accepted, pair combinations of systolic maximum pressure (SW.P_(max))410 and starting diastolic minimum pressure (SW.P_(min1)) 411,characterizing included single pressure waves 408 that are created bythe cardiac beat-induced pressure waves. Said included single pressurewaves 408 (identified by included SW.P_(max) 410/SW.P_(min1) 411 pairs)are shown in FIG. 4 b (termed SW[1], SW[2], SW[3], SW[4], and SW[5]).The ending diastolic minimum pressure (SW.P_(min2)) 412 defining an endof single pressure wave three (SW[3]) is notably not the same asstarting diastolic minimum pressure (SW.P_(min1)) 411 defining the startof single pressure wave four (SW[4]). On the other hand, the endingdiastolic minimum pressure (SW.P_(min2)) 412 defining an end of singlepressure wave four (SW[4]) is the same as the beginning diastolicminimum pressure (SW.P_(min1)) 411 defining the start of single pressurewave five (SW[5]). Said excluded pressure waves 409 (identified asexcluded, i.e. rejected, peak 406/valley 407 pairs) are also indicatedin FIG. 4 b, shown as the two pressure waves between single pressurewave three (SW[3]) and four (SW[4]). A visual inspection of the pressurewaves 402 shown in FIGS. 4 a and 4 b suggest that also the two excludedpressure waves 409 might have been included, given another set of SingleWave & Delta Single Wave Criteria. Thus, the output of the IdentifyingSteps (FIG. 2) applied to the peaks 406 and valleys 407 shown in FIG. 4a and further shown in FIG. 4 b illustrate the fact that the GeneralMethods Criteria and Single Wave & Delta Single Wave Criteria heavilyinfluence the proportion of included single pressure waves 408 andexcluded pressure waves 409.

The time sequence window 405 (TimeSequence[360]) shown in FIGS. 4 a and4 b is one individual of said time sequence windows 405 in a continuousseries of time sequence windows 405 (TimeSequence[1] toTimeSequence[4665]) during this particular recording (Recording[46]):Recording[46].Signal[1].TimeSequence[360].

The method is applied to each of said time sequence windows 405. Tofurther evaluate the included single pressure waves 408 within said timesequence window 405, said Time Sequence & Delta Time Sequence Criteria(FIG. 2) are applied. Only one criterion was applied to the timesequence window 405 shown in FIGS. 4 a and 4 b (TimeSequence[360]),namely that TS.SWCount must be between 4 and 18 counts(=Criteria_ICP_TS.SWCount_SWCountRange). The output of applying saidcriteria are either included, i.e. accepted, or excluded, i.e. rejected,time sequence windows (see FIG. 2), wherein the time sequence windowshown in FIG. 4 b is an included time sequence window.

A subgroup of selected Time Sequence and Delta Time Sequence Criteriadefine whether single pressure waves 408 which occur between twoconsecutive of said time sequence windows 405 are to be placed withinone or the other of said two consecutive individual time sequencewindows. Said selected criteria define that a first of said singlepressure waves (e.g. SW[1]; FIG. 4 b) 408 within said individual timesequence window 405 must have its ending diastolic minimum pressurevalue (SW.P_(min2)) 412 within said individual time sequence window 405.Said selected criteria also define that a last of said single pressurewaves (e.g. SW[5]; FIG. 4 b) 408 within said individual time sequencewindow 405 must have both its starting diastolic minimum pressure value(SW.P_(min1)) 411 and its ending diastolic minimum pressure value(SW.P_(min2)) 412 within said individual time sequence window 405. Onthis basis, the SW.P_(min1) 411/SW.P_(max) 410 pair combinationsubsequent to single pressure wave five (SW[5]) is not included (isdisregarded) within this particular time sequence window 405(TimeSequence[360]). The starting diastolic minimum pressure value(SW.P_(min1)) 411 of this SW.P_(min1) 411/SW.P_(max) 410 pair is thesame as the ending diastolic minimum pressure value (SW.P_(min1)) 412 ofsingle pressure wave five (SW[5]). This SW.P_(min1) 411/SW.P_(max) 410pair was not included within this time sequence window 405 due to noidentified ending diastolic minimum pressure value (SW.P_(min2)) 412within said individual time sequence window 405.

An important issue is how to verify that included single pressure waves408 determined as SW.P_(min1) 411/SW.P_(max) 410 pair combinationscorrespond to cardiac beat-induced pressure waves. The verificationprocess may either be manual or automatic. During manual verification,visual inspection of valley 407/peak 406 pair detections (or SW.P_(min1)411/SW.P_(max) 410 pair detections) is performed for individual signals401 of individual recordings. It is visually inspected how changes insets of criteria (i.e. General Methods Criteria, Single Wave & DeltaSingle Wave Criteria, and/or Time Sequence & Delta Time SequenceCriteria) modify said detections of valley 407/peak 406 pairs and/orSW.P_(min1) 411/SW.P_(max) 410 pairs. During automatic verification,detections of valley 407/peak 406 pairs and/or SW.P_(min1)411/SW.P_(max) 410 pairs may be compared against a reference material.Such a reference material may be another signal within an identicalrecording, wherein samples are obtained from each respective one of saidpressure related signals, each such sample containing a pressure valueat a specific time, and wherein said two or more pressure relatedsignals are all sampled simultaneously.

Examples of such simultaneous pressure-related signals are continuousinvasive intracranial pressure (ICP) and arterial blood pressure (ABP)signals. Instead of a continuous pressure related signal a continuouselectrocardiogram (ECG) signal may as well be selected. A continuouselectrocardiogram (ECG) signal gives a very precise heartbeatcorresponding to the cardiac beat-induced heartbeats. Thus, duringautomatic verification of detections of location of starting diastolicminimum pressure values (SW.P_(min1)) 412, comparisons are made againstlocation of diastolic minimum pressure derived from electrocardiogram(ECG) signals. It should be noted, however, that there could be a delayin time (milliseconds) between sampled ECG and ICP signals caused bynatural technical causes. The ICP's observed SW.P_(min1).Locations willnatural be within a constant delay compared to the ECG's observedSW.P_(min1).Locations. Nevertheless, the heartbeat duration should beidentical. Another method for automatic verification is made byrecalculating all available manually verified recordings whenevercriteria are changed or methodological improvements are made. Such arecalculation can be automatically compared against the referenceresult.

During the Identification Steps the output is included time sequencewindows after application of the Time Sequence & Delta Time SequenceCriteria (see FIG. 2). Based on said included time sequence windows, anumber of parameters: single wave (SW.x)-related parameters, deltasingle pressure wave (ΔSW.x)-related parameters, time sequence(TS.x)-related parameters, and delta time sequence (ΔTS.x)-relatedparameters are obtainable. In the following paragraphs reference isgiven to these parameters.

Reference is now given to FIG. 5 a focusing on single wave(SW.x)-related parameters. Within a continuous pressure-related signal501, one individual of said included single pressure waves 502 is shown:Recording[46].Signal[1].TimeSequence[360].SingleWave[4]. This singlepressure wave is created by one individual cardiac beat. First, itshould be noted that a continuous pressure-related signal 501 involvestwo dimensions, namely a pressure scale 503 and a time scale 504. Bothdimensions have to be considered when describing a continuouspressure-related signal 501. Therefore, single pressure wave(SW.x)-related parameters are with reference to time location andpressure level value. For example, the parameter starting diastolicminimum pressure (SW.P_(min1)) 505 involves the two valuesSW.P_(min1).Location (referring to time stamp location) andSW.P_(min1).Value (referring to pressure level value). This aspect mustbe remembered for the various single pressure wave (SW.x)-relatedparameters, though reference is not specifically given to time locationand pressure level value when referring to these parameters.

The single pressure wave (SW.x)-related parameters computed during saidselected time sequence windows are selected from the group of:

-   -   starting diastolic minimum pressure defining the start of the        single pressure wave (SW.P_(min1)) 505, as further detailed in        equation (1):        SW[n].P _(min1).Value=Signal.Samples[SW[n].P        _(min1).Location]  (1)    -   ending diastolic minimum pressure defining the end of the single        pressure wave (SW.P_(min1)) 506, as further detailed in equation        (2):        SW[n].P _(min2).Value=Signal.Samples[SW[n].P        _(min2).Location]  (2)    -   single wave sample count defining the number of samples within a        single pressure wave, as further detailed in equation (3):        SW[n].SampleCount=SW[n].P _(min2).Location−SW[n].P        _(min1).Location  (3)    -   systolic maximum pressure of the single pressure wave        (SW.P_(max,)) 507, as further detailed in equation (4):        SW[n].P _(max).Value=Signal.Samples[SW[n].P        _(max).Location]  (4)    -   amplitude of the single pressure wave (SW.dP) 508, as further        detailed in equation (5):        SW[n].dP=SW[n].P _(max).Value−SW[n].P _(min1).Value  (5)    -   latency of the single pressure wave (SW.dT) 509, as further        detailed in equation (6):        SW[n].dT=(SW[n].P _(max).Location−SW[n].P        _(min1).Location)/Signal.Frequency  (6)    -   rise time coefficient of the single pressure wave (SW.RT), as        further detailed in equation (7):        SW[n].RT=SW[n].dP/SW[n].dT  (7)    -   wave duration of the single pressure wave (SW.WD) 510, as        further detailed in equation (8):        SW[n].WD=SW[n].SampleCount/Signal.Frequency  (8)    -   mean single wave pressure of the single pressure wave        (SW.Mean_(SW)P), as further detailed in equation (9):

$\begin{matrix}{{{{{SW}\lbrack n\rbrack}.{Mean}_{SW}}P} = \frac{\sum\limits_{{loc} = {{{SW}{\lbrack n\rbrack}}.P_{m\; i\; n\; 1}}}^{{{{SW}{\lbrack n\rbrack}} \cdot P_{m\; i\; n\; 2}} - 1}\left( {{Signal}.{{Samples}\lbrack{loc}\rbrack}} \right)}{{{SW}\lbrack n\rbrack}.{SampleCount}}} & (9)\end{matrix}$

-   -   diastolic minimum pressure difference of the single pressure        wave (SW.Diff_P_(min)) 511, as further detailed in equation        (10):        SW[n].Diff_(—) P _(min) =SW[n].P _(min2).Value−SW[n].P        _(min1).Value  (10)

The ending diastolic minimum pressure (SW.P_(min2)) 506 defines an endof a single pressure wave 502, and the starting diastolic minimumpressure (SW.P_(min1)) 505 defines the start of a single pressure wave502. Said single pressure wave amplitude (=SW.dP) 508 equals systolicmaximum pressure value (SW.P_(max).Value) 507 minus starting diastolicminimum pressure value (SW.P_(min1).Value) 506. Single pressure wavelatency (=SW.dT) 509 equals time duration starting diastolic minimumpressure (SW.P_(min1).Location) 505 to systolic maximum pressure(SW.P_(max).Location) 507. Single pressure rise time coefficient relatesto the relationship between amplitude (SW.dP) 508 and latency (SW.dT)509 (SW.RT=SW.dP/SW.dT). Wave duration (SW.WD) 510 for each individualof said single pressure waves 502 relates to the time duration betweenstarting diastolic minimum pressure (SW.P_(min1)) 505 and endingdiastolic minimum pressure (SW.P_(min2)) 506. Diastolic minimum pressuredifference (SW.Diff_P_(min)) relates to pressure difference betweenstarting and ending diastolic minimum values (SW.P_(min1) 505 versusSW.P_(min2) 506) of one individual of said single pressure waves 502.

Mean single wave pressure (SW.Mean_(SW)P) for each individual of saidsingle pressure waves 502 relates to absolute mean pressure during thetime of the wave duration, i.e. from starting diastolic minimum pressure(SW.P_(min1)) 505 to ending diastolic minimum pressure (SW.P_(min2))506. Mean pressure for an individual single pressure wave(SW.Mean_(SW)P) is the sum of sample values within said pressure wavedivided by numbers of samples.

Except for mean single wave pressure (SW.Mean_(SW)P), all single wave(SW.x)-related parameters are relative values in either pressure ortime. The relative pressure levels are crucial in the way that theserelative values are independent on zero pressure level or drift in zeropressure level.

In FIG. 5 b the first single pressure wave 502 is termed Single Wave[1](n−1) and the subsequent and second single pressure wave 502 is termedSingle Wave[2] (n). As indicated (FIG. 5 b), the ending diastolicminimum pressure (SW.P_(min2)) 506 of Single Wave[1] also is startingdiastolic minimum pressure (SW.P_(min1)) 505 of Single Wave[2]. Inanother situation, the ending diastolic minimum pressure (SW.P_(min2))506 of a first single pressure wave is not the same as startingdiastolic minimum pressure (SW.P_(min1)) 505 of a subsequent secondsingle pressure wave, provided that this second single pressure wave isnot following immediately after the first one.

The delta single pressure wave (ΔSW.x)-related parameters during saidselected time sequence windows are illustrated in FIG. 5 b, wherein theparameters are selected from the group of:

-   -   systolic maximum pressure difference between two subsequent        (n−1;n) single pressure waves (ΔSW.Diff_P_(max)) 512, as further        detailed in equation (11):        ΔSW[n].Diff_(—) P _(max) =SW[n].P _(max).Value−SW[n−1].P        _(max).Value  (11)    -   amplitude difference between two subsequent single pressure        waves (ΔSW.Diff_dP), as further detailed in equation (12):        ΔSW[n].Diff_(—) dP=SW[n].dP−SW[n−1].dP  (12)    -   latency difference between two subsequent single pressure waves        (ΔSW.Diff_dT), as further detailed in equation (13):        ΔSW[n].Diff_(—) dT=SW[n].dT−SW[n−1].DT  (13)    -   rise time coefficient difference between two subsequent single        pressure waves (ΔSW.Diff_RT), as further detailed in equation        (14):        ΔSW[n].Diff_(—) RT=SW[n].RT−SW[n−1].RT  (14)    -   wave duration difference between two subsequent single pressure        waves (ΔSW.Diff_WD), as further detailed in equation (15):        ΔSW[n].Diff_(—) WD=SW[n].WD−SW[n−1].WD  (15)    -   mean single wave pressure difference between two subsequent        single pressure waves (ΔSW.Diff_Mean_(SW)P), as further detailed        in equation (16):        ΔSW[n].Diff_Mean_(SW) P=SW[n].Mean_(SW) P−SW[n−1].Mean_(SW)        P  (16)

Two subsequent single pressure waves (n−1;n) represent a current singlepressure wave SW[n].x in time n subtracted from the previous SW[n−1].xin time n−1 of said individual time sequence window.

As indicated in FIG. 5 b, systolic maximum pressure difference(SW.Diff_P_(max)) 512 relates to pressure difference between systolicmaximum pressure (SW.P_(max)) 507 values of two subsequent of saidsingle pressure waves (SW[2].P_(max)−SW[1].P_(max); FIG. 5 b). Amplitudedifference (SW.Diff_dP) relates to difference in single pressure waveamplitudes (SW.dP) 508 of two subsequent of said single pressure waves(SW[2].dP−SW[1].dP; FIG. 5 b). Latency difference (SW.Diff_dT) relatesto difference in single pressure wave latency (SW.dT) 509 of twosubsequent of said single pressure waves (SW[2].dT−SW[1].dT; FIG. 5 b).Rise time coefficient difference (SW.Diff_RT) relates to difference insingle pressure wave rise time coefficient (SW.RT) of two subsequent ofsaid single pressure waves (SW[2].RT−SW[1].RT; FIG. 5 b). Wave durationdifference (SW.Diff_WD) relates to difference of wave duration (SW.WD)510 between two subsequent of said single pressure waves(SW[2].WD−SW[1].WD; FIG. 5 b). Mean single wave pressure difference(SW.Diff_Mean_(SW)P) relates to difference of mean single wave pressure(Mean_(SW)P) between two subsequent of said single pressure waves(SW[2].Mean_(SW)P−SW[1].Mean_(SW)P; FIG. 5 b). The delta single pressurewave (ΔSW.x)-related parameters are all relative values.

Test recordings show that delta single pressure wave (ΔSW.x)-relatedparameters have an important role in the verification process assessingthe quality of continuous pressure signals. In the presence of badsignal quality, the delta single pressure wave (ΔSW.x)-relatedparameters have greater values than in the presence of good signalquality.

Reference is now given to computation of time sequence (TS.x)-relatedparameters. In FIG. 6 is shown a continuous intracranial pressure (ICP)signal 601, including the included (i.e. accepted) single pressure waves602. The two dimensions of a continuous pressure signal 601 areindicated by the pressure scale 603 and the time scale 604. Oneindividual time sequence window 605 is shown, revealing that theselectable duration was 6 seconds in this case. It is illustrated thatsaid selectable time sequence window 605 is a selected time frame ofsaid sampled signal 601. Time sequence (TS.x)-related parameters arecomputed for each of said individual time sequence windows 605, based onincluded single pressure waves 602 within said individual time sequencewindow 605. For this particular time sequence window 605 there are sevenincluded single pressure waves 602, indicated by the wave durations (WD)of said waves 602 (termed SW[1].WD, SW[2].WD, SW[3].WD, SW[4].WD,SW[5].WD, SW[6].WD, and SW[7].WD). For said included single pressurewaves 602 are as well indicated the amplitudes (SW.dP) (termed SW[1].dP,SW[2].dP, SW[3].dP, SW[4].dP, SW[5].dP, SW[6].dP, and SW[7].dP). Inaddition is shown the systolic maximum pressure difference between twosubsequent (n−1;n) single pressure waves (ΔSW.Diff_P_(max)) (termedΔSW[1].Diff_P_(max), ΔSW[2].Diff_P_(max), ΔSW[3].Diff_P_(max),ΔSW[4].Diff_P_(max), ΔSW[5].Diff_P_(max)) and ΔSW[6].Diff_P_(max),).

According to this invention there is identified a number of said timesequence (TS.x)-related parameters of said single pressure waves 602during individual of said time sequence windows 605, said parametersselected from the group of:

-   -   mean value of starting diastolic minimum pressures of a time        sequence window (TS.MeanP_(min1)), as further detailed in        equation (17):

$\begin{matrix}{{{{TS}\lbrack i\rbrack}.{MeanP}_{m\; i\; n\; 1}} = \frac{\sum\limits_{n = 1}^{{{TS}{\lbrack i\rbrack}}.{SWCount}}{{{SW}\lbrack n\rbrack}.P_{m\; i\; n\; 1}.{Value}}}{{{TS}\lbrack i\rbrack}.{SWCount}}} & (17)\end{matrix}$

-   -   standard deviation of mean value of starting diastolic minimum        pressures of a time sequence window (TS.MeanP_(min1) _(—) STD),        as further detailed in equation (18):

$\begin{matrix}{{{{{TS}\lbrack i\rbrack}.{MeanP}_{m\; i\; n\; 1\_}}{STD}} = \sqrt{\frac{\sum\limits_{n = 1}^{{{TS}{\lbrack i\rbrack}}.{SWCount}}\left( {{{{SW}\lbrack n\rbrack}.P_{m\; i\; n\; 1}.{Value}} - {{{TS}\lbrack i\rbrack}.{MeanP}_{m\; i\; n\; 1}}} \right)^{2}}{{{TS}\lbrack i\rbrack}.{SWCount}}}} & (18)\end{matrix}$

-   -   mean value of systolic maximum pressures of a time sequence        window (TS.MeanP_(max)), as further detailed in equation (19):

$\begin{matrix}{{{{TS}\lbrack i\rbrack}.{MeanP}_{m\; a\; x}} = \frac{\sum\limits_{n = 1}^{{{{TS}{\lbrack i\rbrack}}..}{SWCount}}{{{SW}\lbrack n\rbrack}.P_{m\; a\; x}.{Value}}}{{{TS}\lbrack i\rbrack}.{SWCount}}} & (19)\end{matrix}$

-   -   standard deviation of mean value of systolic maximum pressures        of a time sequence window (TS.MeanP_(max) _(—) STD), as further        detailed in equation (20):

$\begin{matrix}{{{{{TS}\lbrack i\rbrack}.{MeanP}_{m\; a\;{x\_}}}{STD}} = \sqrt{\frac{\sum\limits_{n = 1}^{{{TS}{\lbrack i\rbrack}}.{SWCount}}\left( {{{{{SW}\lbrack n\rbrack}.P_{m\; a\;{x.}}}{Value}} - {{{TS}\lbrack i\rbrack}.{MeanP}_{m\;{ax}}}} \right)^{2}}{{{TS}\lbrack i\rbrack}.{SWCount}}}} & (20)\end{matrix}$

-   -   mean amplitude of a time sequence window (TS.MeandP), as further        detailed in equation (21):

$\begin{matrix}{{{{TS}\lbrack i\rbrack}.{MeandP}} = \frac{\sum\limits_{n = 1}^{{{TS}{\lbrack i\rbrack}}{\ldots SWCount}}\;{{{SW}\lbrack n\rbrack}.{dP}}}{{{TS}\lbrack i\rbrack}.{SWCount}}} & (21)\end{matrix}$

-   -   standard deviation of mean amplitude of a time sequence window        (TS.MeandP_STD), as further detailed in equation (22):        TS[i].MeandP _(—) STD=

$\begin{matrix}\sqrt{\frac{\sum\limits_{n = 1}^{{{TS}{\lbrack i\rbrack}}.{SWCount}}\;\left( {{{{SW}\lbrack n\rbrack}.{dP}} - {{{TS}\lbrack i\rbrack}.{MeandP}}} \right)^{2}}{{{TS}\lbrack i\rbrack}.{SWCount}}} & (22)\end{matrix}$

-   -   mean latency of a time sequence window (TS.MeandT), as further        detailed in equation (23):

$\begin{matrix}{{{{TS}\lbrack i\rbrack}.{MeandT}} = \frac{\sum\limits_{n = 1}^{{{TS}{\lbrack i\rbrack}}.{SWCount}}\;{{{SW}\lbrack n\rbrack}.{dT}}}{{{TS}\lbrack i\rbrack}.{SWCount}}} & (23)\end{matrix}$

-   -   standard deviation of mean latency of a time sequence window        (TS.MeandT_STD), as further detailed in equation (24):

$\begin{matrix}{{{{TS}\lbrack i\rbrack}.{MeandT\_ STD}} = \sqrt{\frac{\sum\limits_{n = 1}^{{{TS}{\lbrack i\rbrack}}.{SWCount}}\;\left( {{{{SW}\lbrack n\rbrack}.{dT}} - {{{TS}\lbrack i\rbrack}.{MeandT}}} \right)^{2}}{{{TS}\lbrack i\rbrack}.{SWCount}}}} & (24)\end{matrix}$

-   -   mean rise time coefficient of a time sequence window        (TS.MeanRT), as further detailed in equation (25):

$\begin{matrix}{{{{TS}\lbrack i\rbrack}.{MeanRT}} = \frac{\sum\limits_{n = 1}^{{{TS}{\lbrack i\rbrack}}.{SWCount}}\;{{{SW}\lbrack n\rbrack}.{RT}}}{{{TS}\lbrack i\rbrack}.{SWCount}}} & (25)\end{matrix}$

-   -   standard deviation of mean rise time coefficient of a time        sequence window (TS.MeanRT_STD), as further detailed in equation        (26):

$\begin{matrix}{{{{TS}\lbrack i\rbrack}.{MeanRT\_ STD}} = \sqrt{\frac{\sum\limits_{n = 1}^{{{TS}{\lbrack i\rbrack}}.{SWCount}}\;\left( {{{{SW}\lbrack n\rbrack}.{RT}} - {{{ST}\lbrack i\rbrack}.{MeanRT}}} \right)^{2}}{{{TS}\lbrack i\rbrack}.{SWCount}}}} & (26)\end{matrix}$

-   -   mean wave duration of a time sequence window (TS.MeanWD), as        further detailed in equation (27):

$\begin{matrix}{{{{TS}\lbrack i\rbrack}.{MeanWD}} = \frac{\sum\limits_{n = 1}^{{{TS}{\lbrack i\rbrack}}.{SWCount}}\;{{{SW}\lbrack n\rbrack}.{WD}}}{{{TS}\lbrack i\rbrack}.{SWCount}}} & (27)\end{matrix}$

-   -   standard deviation of mean wave duration of a time sequence        window (TS.MeanWD_STD), as further detailed in equation (28):

$\begin{matrix}{{{{TS}\lbrack i\rbrack}.{MeanWD\_ STD}} = \sqrt{\frac{\sum\limits_{n = 1}^{{{TS}{\lbrack i\rbrack}}.{SWCount}}\;\left( {{{{SW}\lbrack n\rbrack}.{WD}} - {{{TS}\lbrack i\rbrack}.{MeanWD}}} \right)^{2}}{{{TS}\lbrack i\rbrack}.{SWCount}}}} & (28)\end{matrix}$

-   -   mean single wave pressure of a time sequence window        (TS.Mean_(SW)P), as further detailed in equation (29):

$\begin{matrix}{{{{{TS}\lbrack i\rbrack}.{Mean}_{SW}}P} = \frac{\sum\limits_{n = 1}^{{{TS}{\lbrack i\rbrack}}.{SWCount}}\;{{{{SW}\lbrack n\rbrack}.{Mean}_{SW}}P}}{{{TS}\lbrack i\rbrack}.{SWCount}}} & (29)\end{matrix}$

-   -   standard deviation of mean single wave pressure of a time        sequence window (TS.Mean_(SW)P_STD), as further detailed in        equation (30):

$\begin{matrix}{{{{{TS}\lbrack i\rbrack}.{Mean}_{SW}}{P\_ STD}} = \sqrt{\frac{\sum\limits_{n = 1}^{{{TS}{\lbrack i\rbrack}}.{SWCount}}\;\left( {{{{{SW}\lbrack n\rbrack}.{Mean}_{SW}}P} - {{{{TS}\lbrack i\rbrack}.{Mean}_{SW}}P}} \right)^{2}}{{{TS}\lbrack i\rbrack}.{SWCount}}}} & (30)\end{matrix}$

-   -   mean of diastolic minimum pressure differences of a time        sequence window (TS.MeanDiff_P_(min)), as further detailed in        equation (31):

$\begin{matrix}{{{{TS}\lbrack i\rbrack}.{MeanDiff\_ P}_{\min}} = \frac{\sum\limits_{n = 1}^{{{TS}{\lbrack i\rbrack}}.{SWCount}}\;{{{SW}\lbrack n\rbrack}.{Diff\_ P}_{\min}}}{{{TS}\lbrack i\rbrack}.{SWCount}}} & (31)\end{matrix}$

-   -   standard deviation of mean of diastolic minimum pressure        differences of a time sequence window (TS.MeanDiff_P_(min) _(—)        STD), as further detailed in equation (32):

$\begin{matrix}{{{{{TS}\lbrack i\rbrack}.{MeanDiff\_ P}_{\min}}{\_ STD}} = \sqrt{\frac{\left. {{\sum\limits_{n = 1}^{{{TS}{\lbrack i\rbrack}}.{SWCount}}\;{{{SW}\lbrack n\rbrack}.{Diff\_ P}_{\min}}} - {{{TS}\lbrack i\rbrack}.{MeanDiff\_ P}_{\min}}} \right)^{2}}{{{TS}\lbrack i\rbrack}.{SWCount}}}} & (32)\end{matrix}$

-   -   mean of systolic maximum pressure differences of a time sequence        window (TS.MeanDiff_P_(max)), as further detailed in equation        (33):

$\begin{matrix}{{{{TS}\lbrack i\rbrack}.{MeanDiff\_ P}_{\max}} = \frac{\sum\limits_{n = 2}^{{{TS}{\lbrack i\rbrack}}.{SWCount}}\;{\Delta\;{{{SW}\lbrack n\rbrack}.P_{\max}.{Value}}}}{{{{TS}\lbrack i\rbrack}.{SWCount}} - 1}} & (33)\end{matrix}$

-   -   standard deviation of mean of systolic maximum pressure        differences of a time sequence window (TS.MeanDiff_P_(max) _(—)        STD), as further detailed in equation (34):

$\begin{matrix}{{{{{TS}\lbrack i\rbrack}.{MeanDiff\_ P}_{\max}}{\_ STD}} = \sqrt{\frac{\sum\limits_{n = 2}^{{{TS}{\lbrack i\rbrack}}.{SWCount}}\;\left( {{\Delta\;{{{SW}\lbrack n\rbrack}.P_{\max}.{Value}}} - {{{TS}\lbrack i\rbrack}.{MeanDiff\_ P}_{\max}}} \right)^{2}}{{{{TS}\lbrack i\rbrack}.{SWCount}} - 1}}} & (34)\end{matrix}$

-   -   mean amplitude difference of a time sequence window        (TS.MeanDiff_dP), as further detailed in equation (35):

$\begin{matrix}{{{{TS}\lbrack i\rbrack}.{MeanDiff\_ dP}} = \frac{\sum\limits_{n = 2}^{{{TS}{\lbrack i\rbrack}}.{SWCount}}\;{\Delta\;{{{SW}\lbrack n\rbrack}.{dP}}}}{{{{TS}\lbrack i\rbrack}.{SWCount}} - 1}} & (35)\end{matrix}$

-   -   standard deviation of mean amplitude difference of a time        sequence window (TS.MeanDiff_dP_STD), as further detailed in        equation (36):

$\begin{matrix}{{{{{TS}\lbrack i\rbrack}.{MeanDiff\_ dP}}{\_ STD}} = \sqrt{\frac{\sum\limits_{n = 2}^{{{TS}{\lbrack i\rbrack}}.{SWCount}}\left( {{\Delta\;{{{SW}\lbrack n\rbrack}.{dP}}} - {{{TS}\lbrack i\rbrack}.{MeanDiff\_ dP}}} \right)^{2}}{{{{TS}\lbrack i\rbrack}.{SWCount}} - 1}}} & (36)\end{matrix}$

-   -   mean latency difference of a time sequence window        (TS.MeanDiff_dT), as further detailed in equation (37):

$\begin{matrix}{{{{TS}\lbrack i\rbrack}.{MeanDiff\_ dT}} = \frac{\sum\limits_{n = 2}^{{{TS}{\lbrack i\rbrack}}.{SWCount}}{\Delta\;{{{SW}\lbrack n\rbrack}.{dT}}}}{{{{TS}\lbrack i\rbrack}.{SWCount}} - 1}} & (37)\end{matrix}$

-   -   standard deviation of mean latency difference of a time sequence        window (TS.MeanDiff_dT_STD), as further detailed in equation        (38):

$\begin{matrix}{{{{{TS}\lbrack i\rbrack}.{MeanDiff\_ dT}}{\_ STD}} = \sqrt{\frac{\sum\limits_{n = 2}^{{{TS}{\lbrack i\rbrack}}.{SWCount}}\left( {{\Delta\;{{{SW}\lbrack n\rbrack}.{dT}}} - {{{TS}\lbrack i\rbrack}.{MeanDiff\_ dT}}} \right)^{2}}{{{{TS}\lbrack i\rbrack}.{SWCount}} - 1}}} & (38)\end{matrix}$

-   -   mean rise time coefficient difference of a time sequence window        (TS.MeanDiff_RT), as further detailed in equation (39):

$\begin{matrix}{{{{TS}\lbrack i\rbrack}.{MeanDiff\_ RT}} = \frac{\sum\limits_{n = 2}^{{{TS}{\lbrack i\rbrack}}.{SWCount}}{\Delta\;{{{SW}\lbrack n\rbrack}.{RT}}}}{{{{TS}\lbrack i\rbrack}.{SWCount}} - 1}} & (39)\end{matrix}$

-   -   standard deviation of mean rise time coefficient difference of a        time sequence window (TS.MeanDiff_RT_STD), as further detailed        in equation (40):

$\begin{matrix}{{{{{TS}\lbrack i\rbrack}.{MeanDiff\_ RT}}{\_ STD}} = \sqrt{\frac{\sum\limits_{n = 2}^{{{TS}{\lbrack i\rbrack}}.{SWCount}}\left( {{\Delta\;{{{SW}\lbrack n\rbrack}.{RT}}} - {{{TS}\lbrack i\rbrack}.{MeanDiff\_ RT}}} \right)^{2}}{{{{TS}\lbrack i\rbrack}.{SWCount}} - 1}}} & (40)\end{matrix}$

-   -   mean wave duration difference of a time sequence window        (TS.MeanDiff_WD), as further detailed in equation (41):

$\begin{matrix}{{{{TS}\lbrack i\rbrack}.{MeanDiff\_ WD}} = \frac{\sum\limits_{n = 2}^{{{TS}{\lbrack i\rbrack}}.{SWCount}}{\Delta\;{{{SW}\lbrack n\rbrack}.{WD}}}}{{{{TS}\lbrack i\rbrack}.{SWCount}} - 1}} & (41)\end{matrix}$

-   -   standard deviation of mean wave duration difference of a time        sequence window (TS.MeanDiff_WD_STD), as further detailed in        equation (42):

$\begin{matrix}{{{{{TS}\lbrack i\rbrack}.{MeanDiff\_ WD}}{\_ STD}} = \sqrt{\frac{\sum\limits_{n = 2}^{{{TS}{\lbrack i\rbrack}}.{SWCount}}\left( {{\Delta\;{{{SW}\lbrack n\rbrack}.{WD}}} - {{{TS}\lbrack i\rbrack}.{MeanDiff\_ WD}}} \right)^{2}}{{{{TS}\lbrack i\rbrack}.{SWCount}} - 1}}} & (42)\end{matrix}$

-   -   mean single wave pressure difference of a time sequence window        (TS.MeanDiff_Mean_(SW)P), as further detailed in equation (43):

$\begin{matrix}{{{{{TS}\lbrack i\rbrack}.{MeanDiff\_ Mean}_{sw}}P} = \frac{\sum\limits_{n = 2}^{{TS}.{SWCount}}{\Delta\;{{{SW}\lbrack n\rbrack}.{Mean}_{sw}}P}}{{{{TS}\lbrack i\rbrack}.{SWCount}} - 1}} & (43)\end{matrix}$

-   -   standard deviation of mean single wave pressure difference of a        time sequence window (TS.MeanDiff_Mean_(SW)P_STD), as further        detailed in equation (44):

$\begin{matrix}{{{{{TS}\lbrack i\rbrack}.{MeanDiff\_ P}}{\_ STD}} = \sqrt{\frac{\sum\limits_{n = 2}^{{{TS}{\lbrack i\rbrack}}.{SWCount}}\left( {{\Delta\;{{{SW}\lbrack n\rbrack}.{Mean}_{sw}}P} - {{{{TS}\lbrack i\rbrack}.{MeanDiff\_ Mean}_{sw}}P}} \right)^{2}}{{{{TS}\lbrack i\rbrack}.{SWCount}} - 1}}} & (44)\end{matrix}$

-   -   numbers of accepted single pressure waves of a time sequence        window (TS.SWCount), wherein: TS[i].SWCount=Number of included        single pressure waves within a time sequence window,    -   mean wave amplitude of a time sequence window computed according        to the first matrix (TS.MeanWavedP) (see separate description        with reference to FIG. 8),    -   mean wave latency of a time sequence window computed according        to the first matrix (TS.MeanWavedT) (see separate description        with reference to FIG. 8),    -   mean wave rise time coefficient of a time sequence window        computed according to the second matrix (TS.MeanWaveRT)) (see        separate description with reference to FIG. 8).

Mean amplitude during said time sequence window 605 (TS.MeandP) is thesum of amplitude values for all individual single pressure waves 602during said time sequence window 605 divided by the number of singlepressure waves 602 within said time sequence window 605. With referenceto FIG. 6, the equation is as follows:TS.MeandP=(SW[1].dP+SW[2].dP+SW[3].dP+SW[4].dP+SW[5].dP+SW[6].dP+SW[7].dP)/7.Mean of systolic maximum pressure differences during a time sequencewindow (TS.MeanDiff_P_(max)) is the sum of systolic maximum pressuredifferences between two subsequent (n−1;n) single pressure waves(ΔSW.Diff_P_(max)) values when considering all individual singlepressure waves 602 during said time sequence window 605 divided by thenumber of single pressure waves minus one 602 within said time sequencewindow 605. With reference to FIG. 6, the equation is as follows:TS.MeanDiff_P_(max)=(ΔSW[1].Diff_P_(max)+ΔSW[2].Diff_P_(max)+ΔSW[3].Diff_P_(max)+ΔSW[4].Diff_P_(max)+ΔSW[5].Diff_P_(max)+ΔSW[6].Diff_P_(max))/6.It should be noted that with reference to ΔSW.Diff_P_(max), the symbol dis used in FIG. 6 instead of the symbol Δ. Mean wave duration of a timesequence window (TS.MeanWD) is the sum of wave duration (SW.WD) valuesfor all individual single pressure waves 602 during said time sequencewindow 605 divided by the number of single pressure waves 602 withinsaid time sequence window 605. With reference to FIG. 6, the equation isas follows:TS.MeanWD=(SW[1].WD+SW[2].WD+SW[3].WD+SW[4].WD+SW[5].WD+SW[6].WD+SW[7].WD)/7.

Mean latency during said time sequence window 605 (TS.MeandT) is the sumof latency values for all individual single pressure waves 602 duringsaid time sequence window 605 divided by the number of single pressurewaves 602 within said time sequence window 605. Mean rise timecoefficient during said time sequence window 605 (TS.MeanRT) is the sumof mean rise time coefficient values for all individual single pressurewaves 602 during said time sequence window divided by the numbers ofsingle pressure waves 602 within said time sequence window 605. Meansingle wave pressure during said time sequence window 605(TS.Mean_(SW)P) is the sum of absolute mean pressure, i.e. related towave duration extending from SW.P_(min1).Location toSW.P_(min2).Location-1 for all individual single pressure waves 602during said time sequence window 605 divided by the number of singlepressure waves 602 within said time sequence window 605. It should benoted that mean single wave pressure (TS.Mean_(SW)P) relates to absolutepressure relative to atmospheric pressure.

Mean wave amplitude during said time sequence window 605 (TS.MeanWavedP)is computed according to a first matrix as balanced position in saidmatrix of number of occurrences of amplitude (SW.dP) and latency (SW.dT)values for all individual single pressure waves 602 during said timesequence window 605. Mean wave latency during said time sequence window(TS.MeanWavedT) is computed according to said first matrix as balancedposition in said matrix number of occurrences of amplitude (SW.dP) andlatency (SW.dT) values for all individual single pressure waves 602during said time sequence window 605. Mean wave rise time coefficientduring said time sequence window 605 (TS.MeanWaveRT) is computedaccording to a second matrix as balanced position in said second matrixof number of occurrences of rise time coefficient values (SW.RT) for allindividual single pressure waves 602 during said time sequence window605.

Mean wave duration difference during said time sequence window 605(TS.MeanDiff_WD) is the mean value of all ΔSW.Diff_WD between subsequentsingle pressure waves 602 during said time sequence window 605 dividedby numbers of single pressure waves 602 during said time sequence window605. With reference to FIG. 6, the equation is as follows:TS.MeanDiff_WD=[(SW[2].WD−SW[1].WD)+(SW[3].WD−SW[2].WD)+(SW[4].WD−SW[3].WD)+(SW[5].WD−SW[4].WD)+(SW[6].WD−SW[5].WD)+(SW[7].WD−SW[6].WD)]/6.

The time sequence (TS.x)-related parameters can be considered as the“building blocks” of the inventive method of processing continuouspressure-related signals, and represents a key element of the invention.Time sequence (TS.x) parameters computed from time sequence windows ofgood signal quality (i.e. single pressure waves are created by cardiacbeat-induced pressure waves) provide for completely new informationabout pressure measurements, not revealed by current and prior arttechnology. These important significance areas relate to aspects such ase.g.:

-   a) Quality control. Whether or not the pressure signal is good or    bad is determined by TS.x-related parameters such as e.g.    TS.MeanP_(min1) _(—) STD, TS.MeanP_(max) _(—) STD, TS.MeandP_STD,    TS.MeandT_STD, TS.MeanWD, TS.MeanWD_STD, TS.MeanDiff_P_(min) and    TS.SWCount.-   b) New diagnostic information. Whether or not pressures are    abnormally high or not are determined by TS.x-related parameters    such as e.g. TS.MeandP, TS.MeanRT, TS.Mean_(SW)P, TS.MeanWavedP,    TS.MeanWavedT, and TS.MeanWaveRT. Such information is not derived    from current and prior art technology.-   c) New pressure comparisons method. Two or more simultaneous signals    constituting a pressure recording can be compared to determine    relationships between said signals using said TS.x parameters such    as e.g. TS.MeandP, TS.MeandP_STD, TS.MeanRT, TS.MeanRT_STD,    TS.MeanDiff_dP, TS.MeanDiff_dP_STD.-   d) Relationship determination. Concerning the topic of processing    signals derived from outside or inside a human or animal body    cavity, it is crucial to determine relationships between    TS.x-related parameters involving absolute or relative pressure    values. Examples of TS.x-related parameters including absolute    pressure values are such as TS.MeanP_(min1), TS.MeanP_(max),    TS.Mean_(SW)P. Examples of TS.x-related parameters including    relative pressure values are such as TS.MeandP, TS.MeanRT,    TS.MeanDiff_dP and TS.MeanWavedP. In this context absolute pressure    refers to pressure relative to atmospheric pressure. A challenge not    solved by current and prior art technology is how to obtain useful    diagnostic information from pressure measurements without a zero    pressure level against atmospheric pressure. Computation of    relationships between said TS.x-related parameters represents a    technical solution to said challenge.

It is now focused on delta time sequence (ΔTS.x)-related parameters,with reference to FIG. 7 a. First some remarks should be made concerningFIGS. 7 a and 7 b. In FIG. 7 a is shown a continuous intracranialpressure (ICP) signal (Signal[1]) 701 derived from a sensor within thebrain parenchyma (Location: Intra-dural), and in FIG. 7 b is shown acontinuous intracranial pressure (ICP) signal (Signal[2]) 702 derivedfrom a sensor within the epidural space (Location: Epidural). Both thesesignals were derived from the same one recording Recording[62]; bothsignals were sampled simultaneously with identical time reference. Thetime (x-axis) 703 is identical both for Signal[1] 701 and Signal[2] 702.The pressure scale 704 of Signal[1] 701 (FIG. 7 a) and the pressurescale 705 of Signal[2] 702 (FIG. 7 b) had identical resolution thoughthe absolute pressure levels were different. For both signals are showntwo subsequent time sequence windows, termed Time Sequence[30] 706 (n−1)and Time Sequence[31] 707 (n).

For Signal[1] 701 the amplitudes (SW.dP) of the six included singlepressure waves 708 within the first time sequence window (TimeSequence[30] 706) are numbered 709 (SW[1].dP), 710 (SW[2].dP), 711(SW[3].dP), 712 (SW[4].dP), 713 (SW[5].dP), and 714 (SW[6].dP). ForSignal[1] 701 the amplitudes (SW.dP) of the seven included singlepressure waves 708 within the second time sequence window (TimeSequence[31] 707) are numbered 715 (SW[1].dP), 716 (SW[2].dP), 717(SW[3].dP), 718 (SW[4].dP), 719 (SW[5].dP), 720 (SW[6].dP) and 721(SW[7].dP). For Signal[2] 702 the amplitudes (SW.dP) of the six includedsingle pressure waves 708′ within the first time sequence window (TimeSequence[30] 706) are numbered 722 (SW[1].dP), 723 (SW[2].dP), 724(SW[3].dP), 725 (SW[4].dP), 726 (SW[5].dP), and 727 (SW[6].dP). ForSignal[2] 702 the amplitudes (SW.dP) of the seven included singlepressure waves 708′ within the second time sequence window (TimeSequence[31] 707) are numbered 728 (SW[1].dP), 729 (SW[2].dP), 730(SW[3].dP), 731 (SW[4].dP), 732 (SW[5].dP), 733 (SW[6].dP) and 734(SW[7].dP).

As previously commented on FIGS. 4 a and 4 b, a subgroup of selectedTime Sequence and Delta Time Sequence Criteria define whether singlepressure waves 708, 708′ which occur between two consecutive of saidtime sequence windows [Time Sequence[30] 706 (n−1) versus TimeSequence[31] 707 (n)] are to be placed within one or the other of saidtwo consecutive individual time sequence windows (706, 707). Saidselected criteria define that a first of said single pressure waves(SW[1] 715; FIG. 7 a) 708 within said Time Sequence[31] 707 must haveits ending diastolic minimum pressure value (SW.P_(min2)) within saidTime Sequence[31] 707. Said selected criteria also define that a last ofsaid single pressure waves (SW[7] 721; FIG. 7 a) 708 within said TimeSequence[31] 707 must have both its starting diastolic minimum pressurevalue (SW.P_(min)) and its ending diastolic minimum pressure value(SW.P_(min2)) within said Time Sequence[31] 707. These criteria areintended to illustrate the concept of the invention, not to limit thescope thereof, as other criteria may be used as well or as areplacement.

Reference is now given to FIG. 7 a to illustrate the concept ofcomputing delta time sequence (ΔTS.x)-related parameters betweensubsequent time sequences windows (n/n−1), said parameters are selectedfrom the group of:

-   -   difference of mean values of starting diastolic minimum        pressures between two subsequent time sequence windows        (ΔTS.MeanP_(min1)), as further detailed in equation (45):        ΔTS[i].MeanP _(min1) =TS[i].MeanP _(min1) −TS[i−1].MeanP        _(min1)  (45)    -   standard deviation of difference of mean values of starting        diastolic minimum pressures of two subsequent time sequence        windows (ΔTS.MeanP_(min1) _(—) STD), as further detailed in        equation (46):        ΔTS[i].MeanP _(min1) _(—) STD=TS[i].MeanP _(min1) _(—)        STD−TS[i−1].MeanP _(min1) _(—) STD  (46)    -   difference of mean values of systolic maximum pressure between        two time sequence windows (ΔTS.MeanP_(max)), as further detailed        in equation (47):        ΔTS[i].MeanP _(max) =TS[i].MeanP _(max) −TS[i−1].MeanP        _(max)  (47)    -   standard deviation of difference of mean values of systolic        maximum pressure between two subsequent time sequence windows        (ΔTS.MeanP_(max) _(—) STD), as further detailed in equation        (48):        ΔTS[i].MeanP _(max) _(—) STD=TS[i].MeanP _(max) _(—)        STD−TS[i−1].MeanP _(max) _(—) STD  (48)    -   difference of mean amplitude values between two subsequent time        sequence windows (ΔTS.MeandP), as further detailed in equation        (49):        ΔTS[i].MeandP=TS[i].MeandP−TS[i−1].MeandP  (49)    -   standard deviation of difference of mean amplitudes between two        subsequent time sequence windows (ΔTS.MeandP_STD), as further        detailed in equation (50):        ΔTS[i].MeandP _(—) STD=TS[i].MeandP _(—) STD−TS[i−1].MeandP _(—)        STD  (50)    -   difference of mean latency between two subsequent time sequence        windows (ΔTS.MeandT), as further detailed in equation (51):        ΔTS[i].MeandT=TS[i].MeandT−TS[i−1].MeandT  (51)    -   standard deviation of difference of mean latency between two        subsequent time sequence windows (ΔTS.MeandT_STD), as further        detailed in equation (52):        ΔTS[i].MeandT _(—) STD=TS[i].MeandT _(—) STD−TS[i−1].MeandT _(—)        STD  (52)    -   difference of mean rise time coefficient between two subsequent        time sequence windows (ΔTS.MeanRT), as further detailed in        equation (53):        ΔTS[i].MeanRT=TS[i].MeanRT−TS[i−1].MeanRT  (53)    -   standard deviation of difference of mean rise time coefficient        between two subsequent time sequence windows (ΔTS.MeanRT_STD),        as further detailed in equation (54):        ΔTS[i].MeanRT _(—) STD=TS[i].MeanRT _(—) STD−TS[i−1].MeanRT _(—)        STD  (54)    -   difference of mean wave duration between two subsequent time        sequence windows (ΔTS.MeanWD), as further detailed in equation        (55):        ΔTS[i].MeanWD=TS[i].MeanWD−TS[i−1].MeanWD  (55)    -   standard deviation of difference of mean wave duration between        two subsequent time sequence windows (ΔTS.MeanWD_STD), as        further detailed in equation (56):        ΔTS[i].MeanWD _(—) STD=TS[i].MeanWD _(—) STD−TS[i−1].MeanWD _(—)        STD  (56)    -   difference of mean single wave pressure between two subsequent        time sequence windows (ΔTS.Mean_(SW)P), as further detailed in        equation (57):        ΔTS[i].Mean_(SW) P=TS[i].Mean_(SW) P−TS[i−1].Mean_(SW) P  (57)    -   standard deviation of difference of mean single wave pressure of        two subsequent time sequence windows (ΔTS.Mean_(SW)P_STD), as        further detailed in equation (58):        ΔTS[i].Mean_(SW) P _(—) STD=TS[i].Mean_(SW) P _(—)        STD−TS[i−1].Mean_(SW) P _(—) STD  (58)    -   difference of mean diastolic minimum pressure difference between        two subsequent time sequence windows (ΔTS.MeanDiff_P_(min)), as        further detailed in equation (59):        ΔTS[i].MeanDiff_(—) P _(min) =TS[i].MeanDiff_(—) P _(min)        −TS[i−1].MeanDiff_(—) P _(min)  (59)    -   standard deviation of difference of mean diastolic minimum        pressure difference between two subsequent time sequence windows        (ΔTS.MeanDiff_P_(min) _(—) STD), as further detailed in equation        (60):        ΔTS[i].MeanDiff_(—) P _(min) _(—) STD=TS[i].MeanDiff_(—) P        _(min) _(—) STD−TS[i−1].MeanDiffP _(min) _(—) STD  (60)    -   difference of mean systolic maximum pressure difference between        two subsequent time sequence windows (ΔTS.MeanDiff_P_(max)), as        further detailed in equation (61):        ΔTS[i].MeanDiff_(—) P _(max) =TS[i].MeanDiff_(—) P _(max)        −TS[i−1].MeanDiff_(—) P _(max)  (61)    -   standard deviation of difference of mean systolic maximum        pressure difference between two subsequent time sequence windows        (ΔTS.MeanDiff_P_(max) _(—) STD), as further detailed in equation        (62):        ΔTS[i].MeanDiffP _(max) _(—) STD=TS[i].MeanDiffP _(max) _(—)        STD−TS[i−1].MeanDiffP _(min) _(—) STD  (62)    -   difference of mean amplitude difference between two subsequent        time sequence windows (ΔTS.MeanDiff_dP), as further detailed in        equation (63):        ΔTS[i].MeanDiff_(—) dP=TS[i].MeanDiff_(—)        dP−TS[i−1].MeanDiff_(—) dP  (63)    -   standard deviation of difference of mean amplitude difference        between two subsequent time sequence windows        (ΔTS.MeanDiff_dP_STD), as further detailed in equation (64):        ΔTS[i].MeanDiffdP _(—) STD=TS[i].MeanDiffdP _(—)        STD−TS[i−1].MeanDiffdP _(—) STD  (64)    -   difference of mean latency difference between two subsequent        time sequence windows (ΔTS.MeanDiff_dT), as further detailed in        equation (65):        ΔTS[i].MeanDiff_(—) dT=TS[i].MeanDiff_(—)        dT−TS[i−1].MeanDiff_(—) dT  (65)    -   standard deviation of difference of mean latency difference        between two subsequent time sequence windows        (ΔTS.MeanDiff_dT_STD), as further detailed in equation (66):        ΔTS[i].MeanDiff_(—) dT _(—) STD=TS[i].MeanDiff_(—) dT _(—)        STD−TS[i−1].MeanDiff_(—) dT _(—) STD  (66)    -   difference of mean rise time coefficient difference between two        subsequent time sequence windows (ΔTS.MeanDiff_RT), as further        detailed in equation (67):        ΔTS[i].MeanDiff_(—) RT=TS[i].MeanDiff_(—)        RT−TS[i−1].MeanDiff_(—) RT  (67)    -   standard deviation of difference of mean rise time coefficient        difference between two subsequent time sequence windows        (ΔTS.MeanDiff_RT_STD), as further detailed in equation (68):        ΔTS[i].MeanDiff_(—) RT _(—) STD=TS[i].MeanDiff_(—) RT _(—)        STD−TS[i−1].MeanDiff_(—) RT _(—) STD  (68)    -   difference of mean wave duration difference between two        subsequent time sequence windows (ΔTS.MeanDiff_WD), as further        detailed in equation (69):        ΔTS[i].MeanDiff_(—) WD=TS[i].MeanDiff_(—)        WD−TS[i−1].MeanDiff_(—) WD  (69)    -   standard deviation of difference of mean wave duration        difference between two subsequent time sequence windows        (ΔTS.MeanDiff_WD_STD), as further detailed in equation (70):        ΔTS[i].MeanDiffWD _(—) STD=TS[i].MeanDiffWD _(—)        STD−TS[i−1].MeanDiffWD _(—) STD  (70)    -   difference of mean single wave pressure difference between two        subsequent time sequence windows (ΔTS.MeanDiff_Mean_(SW)P), as        further detailed in equation (71):        ΔTS[i].MeanDiff_Mean_(SW) P=TS[i].MeanDiff_Mean_(SW)        P−TS[i−1].MeanDiff_Mean_(SW) P  (71)    -   standard deviation of difference of mean SW pressure difference        between two subsequent time sequence windows        (ΔTS.MeanDiff_Mean_(SW)P_STD), as further detailed in equation        (72):        ΔTS[i].MeanDiff_Mean_(SW) P _(—) STD=TS[i].MeanDiff_Mean_(SW) P        _(—) STD−TS[i−1].MeanDiff_Mean_(SW) P _(—) STD  (72)    -   difference of single wave count between two subsequent time        sequence windows (ΔTS.SWCount), as further detailed in equation        (73):        ΔTS[i].SWCount=TS[i].SWCount−TS[i−1].SWCount  (73)    -   difference of mean wave amplitude between two subsequent time        sequence windows (ΔTS.MeanWavedP), as further detailed in        equation (74):        ΔTS[i].MeanWavedP=TS[i].MeanWavedP−TS[i−1].MeanWavedP  (74)    -   difference of mean wave latency between two subsequent time        sequence windows (ΔTS.MeanWavedT), as further detailed in        equation (75):        ΔTS[i].MeanWavedT=TS[i].MeanWavedT−TS[i−1].MeanWavedT  (75)    -   difference of mean wave rise time coefficient between two        subsequent time sequence windows (ΔTS.MeanWaveRT), as further        detailed in equation (76):        ΔTS[i].MeanWaveRT=TS[i].MeanWaveRT−TS[i−1].MeanWaveRT  (76)

To further illustrate the concept it is shown the computation ofdifference of mean amplitude values between two subsequent timesequences (ΔTS.MeandP).

With reference to Signal[1] 701 the equation is as follows:ΔTS.MeandP=TS[31].MeandP−TS[30].MeandP. Again, TS[31].x refers to TimeSequence[31] 707 and TS[1].x refers to Time Sequence[30] 706. Withreference to FIG. 7 a, the equation for TS[30].MeandP within TimeSequence[30] 706 is as follows:TS[30].MeandP=(SW[1].dP+SW[2].dP+SW[3].dP+SW[4].dP+SW[5].dP+SW[6].dP)/6(wherein SW[1].dP=709; SW[2].dP=710; SW[3].dP=711; SW[4].dP=712;SW[5].dP=713; SW[6].dP=714). With reference to FIG. 7 a, the equationfor TS[31].MeandP within Time Sequence[31] 707 is as follows:TS[31].MeandP=(SW[1].dP+SW[2].dP+SW[3].dP+SW[4].dP+SW[5].dP+SW[6].dP+SW[7].dP)/7(wherein SW[1].dP=715; SW[2].dP=716; SW[3].dP=717; SW[4].dP=718;SW[5].dP=719; SW[6].dP=720; SW[7].dP=721).

Concerning the inventive step of computing said delta time sequence(ΔTS.x)-related parameters, the major significance relates to qualitycontrol of signal quality. The thresholds and ranges of said ΔTS.xparameters are different in the presence of good signal quality (i.e.single pressure waves are created by cardiac beat-induced pressurewaves) as compared to bad signal quality (i.e. pressure waves arecreated by artifacts or a combination of artifacts or cardiac beatinduced pressure waves). Said delta time sequence (ΔTS.x)-relatedparameters have a key role in excluding (i.e. not accepting) timesequence windows with said bad signal quality.

The strategy for best possible determination of single pressure wavescreated by cardiac beat-induced pressure waves, relates to the inventivestep of determining criteria for the parameters (SW.x, ΔSW.x, TS.x,ΔTS.x) related to time sequence windows of said continuouspressure-related signal. So-called criteria related to said parametersrelate to thresholds and ranges of said parameters. By this inventivestep now commented on in the subsequent paragraphs, a tool is createdfor best possible identification of single pressure waves created bycardiac beat-induced pressure waves.

Reference is now given to said Determining Steps commented on for FIG.2. With reference to the single wave (SW.x)-, delta single wave(ΔSW.x)-, time sequence (TS.x)- and delta time sequence (ΔTS.x)-relatedparameters, criteria are determined, referred to as Single Wave & DeltaSingle Wave Criteria and Time Sequence & Delta Time Sequence Criteria(see FIG. 2).

Single wave criteria relate to criteria for thresholds and ranges ofsaid single pressure wave (SW.x)-related parameters of said singlepressure waves during said time sequence windows, said parametersselected from the group of:

-   -   starting diastolic minimum pressure defining the start of the        single pressure wave (SW.P_(min1)),    -   ending diastolic minimum pressure defining the end of the single        pressure wave (SW.P_(min2)),    -   systolic maximum pressure of the single pressure wave        (SW.P_(max,)),    -   amplitude of the single pressure wave (SW.dP),    -   latency of the single pressure wave (SW.dT),    -   rise time coefficient of the single pressure wave (SW.RT),    -   wave duration of the single pressure wave (SW.WD),    -   mean single wave pressure of the single pressure wave        (SW.Mean_(SW)P), and    -   diastolic minimum pressure difference of the single pressure        wave (SW.Diff_P_(min)).

In general, the notation related to criteria is:Criteria_Type_Location_Parameter. Some examples of Single Wave Criteriaare given, which are specifically related to continuous intracranialpressure (ICP) signals as indicated in the notation:

-   -   Criteria_ICP_Intra-dural_SW.P_(max):        -   a) Systolic maximum pressure of a single pressure wave must            be between −5 to 100 mmHg for pair combinations of            SW.P_(min1) and SW.P_(max) to be included for further            analysis.    -   Criteria_ICP_Intra-dural_SW.dP:        -   a) Amplitude (SW.dP) must be between 1.0 to 35.0 mmHg for            pair combinations of SW.P_(min1) and SW.P_(max) to be            included for further analysis.    -   Criteria_ICP_Intra-dural_SW.dT:        -   a) Latency (SW.dT) must be between 0.10 to 0.40 seconds for            pair combinations of SW.P_(min1) and SW.P_(max) to be            included for further analysis.    -   Criteria_ICP_Intra-dural_SW.WD:        -   a) Wave duration (SW.WD) must be between 0.30 to 1.5 seconds            for pair combinations of SW.P_(min1) and SW.P_(max) to be            included for further analysis.

These examples are not intended to limit the scope of the invention; butto illustrate the substance of said Single Wave Criteria. As indicated,the criteria are related to thresholds and ranges of said singlepressure wave (SW.x)-related parameters during said time sequencewindows. Said criteria determine inclusion, i.e. acceptance, orexclusion, i.e. rejection, of single pressure waves for furtheranalysis; for example minimum-maximum pressure (SW.P_(min1)/SW.P_(max))pairs with said single pressure wave (SW.x)-related parameters outsideselectable thresholds and ranges are excluded (see FIG. 2).

Determining criteria for thresholds and ranges of delta single pressurewave (ΔSW.x)-related parameters between subsequent of said singlepressure waves during said time sequence windows refers to Delta SingleWave Criteria, said criteria are derived from the group of ΔSW.x-relatedparameters of:

-   -   systolic maximum pressure difference between two subsequent        single pressure waves (ΔSW.Diff_P_(max)),    -   amplitude difference between two subsequent single pressure        waves (ΔSW.Diff_dP),    -   latency difference between two subsequent single pressure waves        (SW.Diff_dT),    -   rise time coefficient difference between two subsequent single        pressure waves (ΔSW.Diff_RT),    -   wave duration difference between two subsequent single pressure        waves (ΔSW.Diff_WD), and    -   mean single wave pressure difference between two subsequent        single pressure waves (ΔSW.Diff_Mean_(SW)P).

Some examples of Delta Single Wave Criteria are given, which arespecifically related to continuous intracranial pressure (ICP) signalsas indicated in the notation:

-   -   Criteria_ICP_Intra-dural_ΔSW.P_(max):        -   a) Systolic maximum pressure difference between two            subsequent single pressure waves must be ≦10 mmHg for            combinations of SW.P_(min1) and SW.P_(max) to be included            for further analysis.    -   Criteria_ICP_Intra-dural_ΔSW.WD:        -   a) Wave duration difference between two subsequent single            pressure waves must be ≦0.10 seconds for pair combinations            of SW.P_(min1) and SW.P_(max) to be included for further            analysis.

These examples are not intended to limit the scope of the invention; butto illustrate the substance of said Delta Single Wave Criteria. Saidcriteria for thresholds and ranges of said delta single pressure wave(ΔSW.x)-related parameters between subsequent of said single pressurewaves during said time sequence windows determines inclusion orexclusion of said single pressure waves for further analysis. Saidcriteria exclude minimum-maximum pressure (SW.P_(min1)/SW.P_(max)) pairswith said delta single pressure wave (ΔSW.x)-related parameters outsideselectable thresholds and ranges.

Determining criteria for thresholds and ranges of time sequence(TS.x)-related parameters of said single pressure waves during said timesequence windows refers to Time Sequence Criteria; said criteria arederived from the group of TS.x-related parameters of:

-   -   mean value of starting diastolic minimum pressures of a time        sequence window (TS.MeanP_(min1)),    -   standard deviation of mean value of starting diastolic minimum        pressures of a time sequence window (TS.MeanP_(min1) _(—) STD),    -   mean value of systolic maximum pressures of a time sequence        window (TS.MeanP_(max)),    -   standard deviation of mean value of systolic maximum pressures        of a time sequence window (TS.MeanP_(max) _(—) STD),    -   mean amplitude of a time sequence window (TS.MeandP),    -   standard deviation of mean amplitude of a time sequence window        (TS.MeandP_STD),    -   mean latency of a time sequence window (TS.MeandT),    -   standard deviation of mean latency of a time sequence window        (TS.MeandT_STD),    -   mean rise time coefficient of a time sequence window        (TS.MeanRT),    -   standard deviation of mean rise time coefficient of a time        sequence window (TS.MeanRT_STD),    -   mean wave duration of a time sequence window (TS.MeanWD),    -   standard deviation of mean wave duration of a time sequence        window (TS.MeanWD_STD),    -   mean single wave pressure of a time sequence window        (TS.Mean_(SW)P),    -   standard deviation of mean single wave pressure of a time        sequence window (TS.Mean_(SW)P_STD),    -   mean of diastolic minimum pressure differences of a time        sequence window (TS.MeanDiff_P_(min)),    -   standard deviation of mean of diastolic minimum pressure        differences of a time sequence window (TS.MeanDiff_P_(min) _(—)        STD),    -   mean of systolic maximum pressure differences of a time sequence        window (TS.MeanDiff_P_(max)),    -   standard deviation of mean of systolic maximum pressure        differences of a time sequence window (TS.MeanDiff_P_(max) _(—)        STD),    -   mean amplitude difference of a time sequence window        (TS.MeanDiff_dP),    -   standard deviation of mean amplitude difference of a time        sequence window (TS.MeanDiff_dP_STD),    -   mean latency difference of a time sequence window        (TS.MeanDiff_dT),    -   standard deviation of mean latency difference of a time sequence        window (TS.MeanDiff_dT_STD),    -   mean rise time coefficient difference of a time sequence window        (TS.MeanDiff_RT),    -   standard deviation of mean rise time coefficient difference of a        time sequence window (TS.MeanDiff_RT_STD),    -   mean wave duration difference of a time sequence window        (TS.MeanDiff_WD),    -   standard deviation of mean wave duration difference of a time        sequence window (TS.MeanDiff_WID_STD),    -   mean single wave pressure difference of a time sequence window        (TS.MeanDiff_Mean_(SW)P),    -   standard deviation of mean single wave pressure difference of a        time sequence window (TS.MeanDiff_Mean_(SW)P_STD),    -   numbers of included single pressure waves of a time sequence        window (TS.SWCount),    -   mean wave amplitude of a time sequence window (TS.MeanWavedP),    -   mean wave latency of a time sequence window (TS.MeanWavedT), and    -   mean wave rise time coefficient of a time sequence window        (TS.MeanWaveRT).

Some examples of Time Sequence Criteria are given, which arespecifically related to continuous intracranial pressure (ICP) signalsas indicated in the notation:

-   -   Criteria_ICP_Intra-dural_TS.SWCount:        -   a) Number of included single pressure waves of a time            sequence window must be between 4 and 18 for said time            sequence window to be included (accepted) for further            analysis.    -   Criteria_ICP_Intra-dural_TS.MeanP_(max):        -   a) Mean value of systolic maximum pressures of a time            sequence window must be between 2 and 100 mmHg for said time            sequence window to be included for further analysis.

The intention of these examples of Time Sequence Criteria are toillustrate the substance of said criteria, though the specific valuesare not intended to limit the scope of the invention. Time SequenceCriteria for thresholds and ranges of time sequence (TS.x)-relatedparameters of single pressure waves during time sequence windowsdetermines inclusion, i.e. acceptance, or exclusion, i.e. rejection, ofsaid time sequences for further analysis. The criteria exclude forfurther analysis time sequences with time sequence (TS.x)-relatedparameters outside selectable thresholds and ranges.

Determining criteria for thresholds and ranges of delta time sequence(ΔTS.x)-related parameters between subsequent time sequences refers toDelta Time Sequence Criteria; said criteria are derived from the groupof ΔTS.x-related parameters of:

-   -   difference of mean values of starting diastolic minimum        pressures between two subsequent time sequence windows        (ΔTS.MeanP_(min1)),    -   standard deviation of difference of mean values of starting        diastolic minimum pressures of two subsequent time sequence        windows (ΔTS.MeanP_(min1) _(—) STD),    -   difference of mean values of systolic maximum pressure between        two subsequent time sequence windows (ΔTS.MeanP_(max)),    -   standard deviation of difference of mean values of systolic        maximum pressure between two subsequent time sequence windows        (ΔTS.MeanP_(max) _(—) STD),    -   difference of mean amplitude values between two subsequent time        sequence windows (ΔTS.MeandP),    -   standard deviation of difference of mean amplitudes between two        subsequent time sequence windows (ΔTS.MeandP_STD),    -   difference of mean latency between two subsequent time sequence        windows (ΔTS.MeandT),    -   standard deviation of difference of mean latency between two        subsequent time sequence windows (ΔTS.MeandT_STD),    -   difference of mean rise time coefficient between two subsequent        time sequence windows (ΔTS.MeanRT),    -   standard deviation of difference of mean rise time coefficient        between two subsequent time sequence windows (ΔTS.MeanRT_STD),    -   difference of mean wave duration between two subsequent time        sequence windows (ΔTS.MeanWD),    -   standard deviation of difference of mean wave duration between        two subsequent time sequence windows (ΔTS.MeanWD_STD),    -   difference of mean single wave pressure between two subsequent        time sequence windows (ΔTS.Mean_(SW)P),    -   standard deviation of difference of mean single wave pressure of        two subsequent time sequence windows (ΔTS.Mean_(SW)P_STD),    -   difference of mean diastolic minimum pressure difference between        two subsequent time sequence windows (ΔTS.MeanDiff_P_(min)),    -   standard deviation of difference of mean diastolic minimum        pressure difference between two subsequent time sequence windows        (ΔTS.MeanDiff_P_(min) _(—) STD),    -   difference of mean systolic maximum pressure difference between        two subsequent time sequence windows (ΔTS.MeanDiff_P_(max)),    -   standard deviation of difference of mean systolic maximum        pressure difference between two subsequent time sequence windows        (ΔTS.MeanDiff_P_(max) _(—) STD),    -   difference of mean amplitude difference between two subsequent        time sequence windows (ΔTS.MeanDiff_dP),    -   standard deviation of difference of mean amplitude difference        between two subsequent time sequence windows        (ΔTS.MeanDiff_dP_STD),    -   difference of mean latency difference between two subsequent        time sequence windows (ΔTS.MeanDiff_dT),    -   standard deviation of difference of mean latency difference        between two subsequent time sequence windows        (ΔTS.MeanDiff_dT_STD),    -   difference of mean rise time coefficient difference between two        subsequent time sequence windows (ΔTS.MeanDiff_RT),    -   standard deviation of difference of mean rise time coefficient        difference between two subsequent time sequence windows        (ΔTS.MeanDiff_RT_STD),    -   difference of mean wave duration difference between two        subsequent time sequence windows (ΔTS.MeanDiff_WD),    -   standard deviation of difference of mean wave duration        difference between two subsequent time sequence windows        (ΔTS.MeanDiff_WD_STD),    -   difference of mean single wave pressure difference between two        subsequent time sequence windows (ΔTS.MeanDiff_Mean_(SW)P),    -   standard deviation of difference of mean SW pressure difference        between two subsequent time sequence windows        (ΔTS.MeanDiff_Mean_(SW)P_STD),    -   difference of single wave count between two subsequent time        sequence windows (ΔTS.SWCount),    -   difference of mean wave amplitude between two subsequent time        sequence windows (ΔTS.MeanWavedP),    -   difference of mean wave latency between two subsequent time        sequence windows (ΔTS.MeanWavedT), and    -   difference of mean wave rise time coefficient between two        subsequent time sequence windows (ΔTS.MeanWaveRT).

Some examples of Delta Time Sequence Criteria are given, which arespecifically related to continuous intracranial pressure (ICP) signalsas indicated in the notation:

-   -   Criteria_ICP_Intra-dural_ΔTS.SWCount:        -   a) Difference of single wave count between two subsequent            time sequences must be ≦2 for said time sequence window to            be included for further analysis.    -   Criteria_ICP_Intra-dural_ΔTS.MeanDiff_dP:        -   a) Difference of mean amplitude between two subsequent time            sequences must be ≦5 mmHg for said time sequence window to            be included for further analysis.

The intention of these examples of Delta Time Sequence Criteria are toillustrate the substance of said criteria, though the specific valuesare not intended to limit the scope of the invention. Delta TimeSequence Criteria for thresholds and ranges of time sequence(ΔTS.x)-related parameters between subsequent time sequence windowsdetermine inclusion or exclusion of said time sequences for furtheranalysis. The criteria exclude time sequence windows with said deltatime sequence (ΔTS.x)-related parameters outside selectable thresholdsand ranges.

It is differentiated between static and dynamic criteria. Staticcriteria for thresholds and ranges of said SW.x-, ΔSW.x-, TS.x- andΔTS.x-related parameters are unchangeable during a recording. Dynamiccriteria for said thresholds and ranges of said SW.x-, ΔSW.x-, TS.x- andΔTS.x-related parameters are changeable during a recording.

The major application of determination of Single Wave & Delta SingleWave Criteria and Time Sequence & Delta Time Sequence Criteria areoptimal identification of single pressure waves related to cardiacbeat-induced pressure waves and identification of pressure waves relatedto artifacts or a combination of artifacts and cardiac beat-inducedpressure waves. This represents an optimal differentiating betweensingle pressure waves caused by cardiac beat-induced pressure waves andpressure waves caused by artifact-induced pressure waves or acombination thereof.

The determination of Single Wave & Delta Single Wave Criteria and TimeSequence & Delta Time Sequence Criteria should be considered aniterative process. This is explained with reference to the experience ofthe inventor. When analyzing continuous intracranial pressure (ICP)signals during the first years, the inventor used only the followingcriteria (i.e. Single Wave Criteria and Time Sequence Criteria):

-   -   Criteria_ICP_Intra-dural_SW.dP:        -   a) Amplitude (SW.dP) must be between 1.0 to 35.0 mmHg for            pair combinations of SW.P_(min1) and SW.P_(max) to be            included for further analysis.    -   Criteria_ICP_Intra-dural SW.dT:        -   a) Latency (SW.dT) must be between 0.10 to 0.40 seconds for            pair combinations of SW.P_(min1) and SW.P_(max) to be            included for further analysis.    -   Criteria_ICP_Intra-dural TS.SWCount:        -   a) Number of included single pressure waves of a time            sequence window must be between 4 and 18 for said time            sequence window to be included for further analysis.

Thus, with reference to the Identifying Steps shown in FIG. 2., includedsingle pressure waves were identified by SW.P_(max)/SW.P_(min1) paircombinations wherein the calculated amplitude (SW.dP) values werebetween 1.0 and 35.0 mmHg, and the calculated latency (SW.dT) valueswere between 0.10 and 0.40 seconds. Furthermore, the included timesequence windows contained between 4 and 18 included single pressurewaves. Several hundreds of continuous intracranial pressure (ICP)recordings were obtained and stored in the database as raw data files bythis approach. For each of these continuous intracranial pressure (ICP)recordings, the different SW.x-, ΔSW.x-, TS.x- and ΔTS.x-relatedparameters were computed. The method of computing said parameters isapplied to each of said time sequence windows in a continuous series ofsaid time sequence windows during a recording. By this approach areference material is established concerning the normal distribution ofsaid SW.x-, ΔSW.x-, TS.x- and ΔTS.x-related parameters. Based on thisnormal distribution, new and improved criteria are determined forthresholds and ranges of said SW.x-, ΔSW.x-, TS.x- and ΔTS.x-relatedparameters during said time sequence windows (or more correctly betweensaid time sequence windows for ΔTS.x-related parameters). The newthresholds and ranges of said Single Wave & Delta Single Wave Criteriaand Time Sequence & Delta Time Sequence Criteria are applied whenanalyzing new continuous intracranial pressure (ICP) signals. Inaddition, the raw data files of continuous intracranial pressure (ICP)signals stored in said database may be run through the Identifying Stepsand Computing Steps shown in FIG. 2 once again, and both manual andautomatic verification of the output being performed.

The Identifying Steps, Computing Steps and Determining Steps describedin FIG. 2 provide a new and inventive method for processing continuouspressure-related signals derived from locations inside or outside ahuman or animal body or body cavity. Said inventive method (referred toas a first feature of this invention has great significance as shortlysummarized below:

Time sequence (TS.x)-related parameters can be computed from includedtime sequence windows wherein said time sequence windows are includedthe best possible way. Said time sequence windows contain singlepressures waves related to cardiac beat-induced pressure waves, withless impact of pressure waves caused by artifacts or a combination ofartifacts and cardiac beat-induced pressure waves. Thereby the risk ofcomputing false or misleading time sequence (TS.x)-related parameters ismade minimal.

Related time sequence (rTS.x) parameters can be determined from includedtime sequence windows wherein said time sequence windows are includedthe best possible way. Time sequence windows from simultaneous signalswithin a recording contain single pressure waves related to cardiacbeat-induced pressure waves, with less impact of pressure waves createdby artifacts or a combination of artifacts and cardiac beat-inducedpressure waves. Obviously, it has no meaning to compute related timesequence (rTS.x) parameters from time sequence windows containing a highproportion of artifacts. This aspect is crucial for the inventive methodfurther detailed in independent claim 1 and subsequent sub-claims 2-25.

According to the present invention there is provided for a device and asystem, which enable inter alia more controlled drainage of excess fluidfrom a brain or spinal fluid cavity to another body cavity of a humanbeing or animal. However, there are also aspects linked to the use ofsaid device and system. As appreciated, said device and system includecomponents making use of one or more of the inventive methods accordingto the invention to yield intended output from the device or system. Akey element concerning these features of the invention is thecomputation of time sequence (TS.x)-related parameters. Obviously, it iscrucial that time sequence (TS.x)-related parameters are computed fromtime sequence windows that are included the best possible way (i.e. timesequence windows containing single pressures waves related to cardiacbeat-induced pressure waves, not to artifacts or a combination ofartifacts and cardiac beat-induced pressure waves). Computation of falseor misleading time sequence (TS.x)-related parameters would cause wrongregulation of drainage of fluid from said brain or spinal fluid cavity.

The method of computing time sequence (TS.x)-related parameters has akey role in other described devices and system according to thisinvention. The invention describes a display device(s), a sensor device(s) and combined sensor-display device(s) for use in sensing continuouspressure-related signals derivable from locations inside or outside ahuman or animal body or body cavity. The invention also describes asystem for processing continuous pressure-related signals derivable fromone or more sensor(s) having location(s) inside or outside a body orbody cavity of a human being. As related to these features of theinvention, it is crucial that time sequence (TS.x)-related parametersare computed from time sequence windows that are included the bestpossible way (i.e. time sequence windows containing single pressureswaves related to cardiac beat-induced pressure waves, not to artifactsor a combination of artifacts and cardiac beat-induced pressure waves).Computation of false or misleading time sequence (TS.x)-relatedparameters would cause wrong measurements from said sensor device(s) anddisplay wrong information from said display device (s) or combinedsensor-display device(s).

Reference is now given to the second feature of this invention. Inparticular, characteristics of said second feature are illustrated inFIG. 8, and also in FIGS. 4 and 7.

Said second feature of the invention is related to a method forprocessing continuous pressure-related signals derivable from locationsinside or outside a human or animal body or body cavity, comprising thesteps of obtaining samples of said signals at specific intervals, andconverting thus sampled pressure signals into pressure-related digitaldata with a time reference. For selectable time sequence windows themethod comprises the further steps of identifying from said digital datasignal single pressure waves related to cardiac beat-induced pressurewaves, and identifying from said digital data signal pressure wavesrelated to artifacts or a combination of artifacts and cardiacbeat-induced pressure waves. The method also relates to computing timesequence (TS.x)-related parameters of said single pressure waves duringindividual of said time sequence windows, and establishing an analysisoutput selected from one or more of said time sequence (TS.x)-relatedparameters of said single pressure waves during individual of said timesequence windows, said one or more parameters selectable from: Mean waveamplitude (TS.MeanWavedP), mean wave latency (TS.MeanWavedT), mean waverise time coefficient (TS.MeanWaveRT), mean amplitude (TS.MeandP), meanlatency (TS.MeandT), mean rise time coefficient (TS.MeanRT), and meansingle wave pressure (TS.Mean_(SW)P).

Test recordings have shown that said time sequence (TS.x)-relatedparameters provide completely new information from continuouspressure-related signals, not revealed by current and prior arttechnology. Said new information is shortly summarized:

Time sequence (TS.x)-related parameters (e.g. such as TS.MeanWavedP,TS.MeanWavedT, TS.MeanWaveRT, TS.MeandP, TS.MeandT, TS.MeanRT,TS.Mean_(SW)P) provide completely new information whether pressures areabnormally high or not. For example, reduced cerebral compliance, i.e.abnormally high intracranial pressure (ICP), is not revealed by knowntechnology, but is revealed by said TS.x parameters.

Time sequence (TS.x)-related parameters (e.g. such as TS.MeanWavedP,TS.MeanWavedT, TS.MeanWaveRT, TS.MeandP, TS.MeandT, TS.MeanRT,TS.Mean_(SW)P) provide completely new information whether a pressuremeasurement is of good or bad quality. For example, sensor error orwrong sensor placement may not always be detected by known technology,but are revealed by said TS.x parameters.

Time sequence (TS.x)-related parameters (e.g. such as TS.MeanWavedP,TS.MeanWavedT, TS.MeanWaveRT, TS.MeandP, TS.MeandT, TS.MeanRT,TS.Mean_(SW)P) provide completely new information whether pressures arechanging during treatment (surgical or medical). A change in pressuremay be impossible to reveal according to known and prior art technology.For example, a change in intracranial pressure (ICP) following medicalor surgical treatment may be impossible to reveal by current technology,though pressure changes are shown by said TS.x parameters.

Time sequence (TS.x)-related parameters (e.g. such as TS.MeanWavedP,TS.MeanWavedT, TS.MeanWaveRT, TS.MeandP, TS.MeandT, TS.MeanRT) involvingrelative values are not influenced by a zero pressure level relative toatmospheric pressure or drift of sensor zero pressure level. This factrepresents a technical solution as compared to current technology, inwhich such problems are important concerning calibration against a zeropressure level or drift of sensor pressure level.

An overview of said method for analyzing continuous pressure-relatedsignals 801 derived from within or outside a human or animal body orbody cavity is given in FIG. 8, and reference is now given to FIG. 8.

In short, the method incorporates different Identifying Steps, ComputingStep and Presentation Step. Said Identifying Steps includeidentification of all separate peaks and valleys 802 in said sampledsignal 801. Each of said peaks is a sample with a pressure value and atime stamp or location, and each of said valleys is a sample with apressure value and a time stamp or location. The result of applyingGeneral Methods Criteria 803 is either included, i.e. accepted,peak/valley pair combinations 804 or excluded, i.e. rejected,peak/valley pair combinations 805.

After applying the Single Wave & Delta Single Wave Criteria 806 to saidincluded peak/valley pairs 804, the output is either included singlepressure waves 807 or excluded pressure waves 808. Said criteria 806relate to thresholds and ranges of single pressure wave (SW.x)-relatedparameters and delta single pressure wave (ΔSW.x)-related parametersduring time sequence windows.

After applying the Single Wave & Delta Single Wave Criteria 806,included pair combinations of peak/valley pairs 804 in said signal 801correspond to included single pressure waves 807. Pair combinations ofdiastolic minimum pressure (SW.P_(min1)) and systolic maximum pressure(SW.P_(max)) characterize single pressure waves created by cardiacbeat-induced pressure waves. Said criteria 806 exclude for furtheranalysis pressure waves (i.e. minimum-maximum pressure(SW.P_(min1)/SW.P_(max)) pairs) during said time sequence windows withsaid single pressure wave (SW.x)- and delta single pressure wave(ΔSW.x)-related parameters outside selected criteria for thresholds andranges of said parameters. Said criteria 806 include for furtheranalysis single pressure waves 807 having single pressure wave (SW.x)-and delta single pressure wave (ΔSW.x)-related parameters withinselected criteria for thresholds and ranges of said single pressure wave(SW.x)-related parameters. Pair combinations of diastolic minimumpressure (SW.P_(min1)) and systolic maximum pressure (SW.P_(max))correspond to diastolic minimum pressures and systolic maximum pressuresof individual of pressure waves created by each of said cardiac beats.

In order to further evaluate the included single pressure waves 807,Time Sequence & Delta Time Sequence Criteria 809 are applied to each ofsaid time sequence windows in a continuous series of said time sequencewindows during a recording. Each time sequence window is a selected timeframe of said signal. Said criteria 809 for thresholds and ranges ofsaid time sequence (TS.x) and delta time sequence (ΔTS.x)-relatedparameters determine included time sequence windows 810 and excludedtime sequence windows 811. Said criteria 809 exclude for furtheranalysis time sequence windows 811 with time sequence (TS.x)- and deltatime sequence (ΔTS.x)-related parameters outside selected criteria forthresholds and ranges of said parameters. Said criteria 809 include forfurther analysis time sequence windows 810 having time sequence (TS.x)and delta time sequence (ΔTS.x)-related parameters within selectedcriteria for thresholds and ranges of said time sequence (TS.x) anddelta time sequence (ΔTS.x)-related parameters.

In a next Computing Step is computed time sequence (TS.x)-relatedparameters 812 for each individual of said included time sequencewindows 810. The Identifying Steps are applied to each of said timesequence windows in a continuous series of said time sequence windowsduring a recording. The Computing Step is applied to each of saidincluded time sequence windows 810 in a continuous series of said timesequence windows during a recording. In a final Presentation Step, ananalysis output is established selected from one or more of said timesequence (TS.x)-related parameters: Mean wave amplitude (TS.MeanWavedP),mean wave latency (TS.MeanWavedT), mean wave rise time coefficient(TS.MeanWaveRT), mean amplitude (TS.MeandP), mean latency (TS.MeandT),mean rise time coefficient (TS.MeanRT), and mean single wave pressure(TS.Mean_(SW)P). Said analysis output for selected ones of said timesequence (TS.x)-related parameters includes different types of datapresentation, such as repetitive numerical presentation, trend plotpresentation, histogram presentation, and quantitative matrixpresentation.

Concerning the Identifying Steps, the process method is identical as theprocess described for FIGS. 2, 3 a, 3 b, 3 c, 4 a, 4 b, 5 a, 5 b, 6, and7 a. Details about General Methods Criteria 803, Single Wave & DeltaWave Criteria 806 and Time Sequence & Delta Time Sequence Criteria 809are already commented on in detail with reference to FIG. 2, andtherefore these aspects are not commented on further in this context.

The output of the Identifying Steps is included, i.e. accepted, timesequence windows wherein said time sequence windows 810 are included thebest possible way. This means that these time sequence windows 810contain single pressure waves related to cardiac beat-induced pressurewaves, not to artifacts or a combination of artifacts and cardiacbeat-induced pressure waves. Thereby the risk of computing false ormisleading time sequence (TS.x)-related parameters is made minimal.

In the Computing Step, time sequence (TS.x)-related parameters 812 fromincluded time sequences windows 810 are computed, said parametersselected from the group of:

-   -   mean value of starting diastolic minimum pressures of a time        sequence window (TS.MeanP_(min1)),    -   standard deviation of mean value of starting diastolic minimum        pressures of a time sequence window (TS.MeanP_(min1) _(—) STD),    -   mean value of systolic maximum pressures of a time sequence        window (TS.MeanP_(max)),    -   standard deviation of mean value of systolic maximum pressures        of a time sequence window (TS.MeanP_(max) _(—) STD),    -   mean amplitude of a time sequence window (TS.MeandP),    -   standard deviation of mean amplitude of a time sequence window        (TS.MeandP_STD),    -   mean latency of a time sequence window (TS.MeandT),    -   standard deviation of mean latency of a time sequence window        (TS.MeandT_STD),    -   mean rise time coefficient of a time sequence window        (TS.MeanRT),    -   standard deviation of mean rise time coefficient of a time        sequence window (TS.MeanRT_STD),    -   mean wave duration of a time sequence window (TS.MeanWD),    -   standard deviation of mean wave duration of a time sequence        window (TS.MeanWD_STD),    -   mean single wave pressure of a time sequence window        (TS.Mean_(SW)P),    -   standard deviation of mean single wave pressure of a time        sequence window (TS.Mean_(SW)P_STD),    -   mean of diastolic minimum pressure differences of a time        sequence window (TS.MeanDiff_P_(min)),    -   standard deviation of mean of diastolic minimum pressure        differences of a time sequence window (TS.MeanDiff_P_(min) _(—)        STD),    -   mean of systolic maximum pressure differences of a time sequence        window (TS.MeanDiff_P_(max)),    -   standard deviation of mean of systolic maximum pressure        differences of a time sequence window (TS.MeanDiff_P_(max) _(—)        STD),    -   mean amplitude difference of a time sequence window        (TS.MeanDiff_dP),    -   standard deviation of mean amplitude difference of a time        sequence window (TS.MeanDiff_dP_STD),    -   mean latency difference of a time sequence window        (TS.MeanDiff_dT),    -   standard deviation of mean latency difference of a time sequence        window (TS.MeanDiff_dT_STD),    -   mean rise time coefficient difference of a time sequence window        (TS.MeanDiff_RT),    -   standard deviation of mean rise time coefficient difference of a        time sequence window (TS.MeanDiff_RT_STD),    -   mean wave duration difference of a time sequence window        (TS.MeanDiff_WD),    -   standard deviation of mean wave duration difference of a time        sequence window (TS.MeanDiff_WD_STD),    -   mean single wave pressure difference of a time sequence window        (TS.MeanDiff_Mean_(SW)P),    -   standard deviation of mean single wave pressure difference of a        time sequence window (TS.MeanDiff_Mean_(SW)P_STD),    -   numbers of accepted single pressure waves of a time sequence        window (TS.SWCount),    -   mean wave amplitude of a time sequence window computed according        to the first matrix (TS.MeanWavedP),    -   mean wave latency of a time sequence window computed according        to the first matrix (TS.MeanWavedT),    -   mean wave rise time coefficient of a time sequence window        computed according to the second matrix (TS.MeanWaveRT).

In particular, an analysis output is established selected from one ormore of said time sequence (TS.x)-related parameters 812: Mean waveamplitude (TS.MeanWavedP), mean wave latency (TS.MeanWavedT), mean waverise time coefficient (TS.MeanWaveRT), mean amplitude (TS.MeandP), meanlatency (TS.MeandT), mean rise time coefficient (TS.MeanRT), and meansingle wave pressure (TS.Mean_(SW)P). The computation of these timesequence (TS.x)-related parameters is now commented on.

The computation of mean wave amplitude (TS.MeanWavedP) and mean wavelatency (TS.MeanWavedT) is now described. This analysis comprises thesteps of creating a first matrix (Table 1) based on determining numbersof single pressure waves with pre-selected values related to amplitude(SW.dP) and latency (SW.dT), one axis of the matrix being related to anarray of pre-selected values of pressure amplitude (SW.dP) and the otheraxis of the matrix being related to an array of pre-selected values oflatencies (SW.dT), and indicating for each matrix cell at respectiveintersections in said first matrix a number of occurrence of matchesbetween a specific pressure amplitude (SW.dP) and a specific latency(SW.dT) related to successive measurements of single pressure waves oversaid individual time sequence windows. The occurrence of matches in saidmatrix is indicated through actual number of matches during individualof said time sequence windows. The single pressure wave parameters ofamplitude (SW.dP) and latency (SW.dT) are categorized into groups, saidgroups reflecting ranges of said single wave (SW.x)-related parametervalues. The method comprises the further step of computing balancedposition for a number of occurrences of said single pressure wave(SW.x)-related parameters amplitude (SW.dP) and latency (SW.dT) valuesduring individual of said time sequence windows in said first matrix.The balanced position of said first matrix of numbers of amplitude(SW.dP) and latency (SW.dT) combinations corresponds to mean waveamplitude (TS.MeanWavedP) and mean wave latency (TS.MeanWavedT) duringsaid individual time sequence windows.

A detailed example of computing the mean wave amplitude (TS.MeanWavedP)and mean wave latency (TS.MeanWavedT) is now given with reference toFIG. 4 b. The mathematical process is intended to illustrate theconcept, not to limit the scope of the invention. The process ofdetermining the included single pressure waves 408 within the timesequence window 405 was described in connection with FIGS. 4 a and 4 b.The included, i.e. accepted, single pressure waves 408 (identified byincluded SW.P_(max) 410/SW.P_(min1) 411 pairs) during the time sequencewindow shown 405 shown in FIG. 4 b (Time Sequence[360]) are termedSW[1], SW[2], SW[3], SW[4], and SW[5]. For each of these single pressurewaves 408 both the amplitude (SW.dP) and latency (SW.dT) values werecomputed. The number of occurrences of single pressure waves 408 withcertain amplitude (SW.dP) and latency (SW.dT) combinations are plottedinto a first matrix that is two-dimensional. A part of such a matrix ispresented in Table 1, illustrating the distribution of amplitude(SW.dP)/latency (SW.dT) combinations of the five included singlepressure waves 408 (SW[1], SW[2], SW[3], SW[4], and SW[5]) presented inFIG. 4 b. For example, single pressure waves 408 with amplitude (SW.dP)values greater or equal to 2.5 mmHg but less than 3.0 mmHg and latency(SW.dT) values greater or equal to 0.10 seconds, but less than 0.11seconds occurred once during the time sequence window 405 of 6 secondsshown in said first matrix (Time Sequence[360]). The amplitude (SW.dP)values are presented in the columns and the latency (SW.dT) values inthe rows. The matrix shown in Table 1 represents only a small fractionof a matrix of 1800 cells. A total of 60 amplitude (SW.dP) groups werecreated using a range of amplitudes (SW.dP) equal to 0 to 30.0 mmHg,with intervals of 0.5 mmHg, giving a total of 60 columns. A total of 30latency (SW.dT) groups were created using a range of latencies (SW.dT)from 0.10 to 0.40 seconds with intervals 0.01 seconds, giving a total of30 rows. For example, the first column corresponds to the firstamplitude (SW.dP) group, named 0.5 (corresponding to 0.5 mmHg); thisgroup includes amplitude (SW.dP) values greater or equal to 0.5 mmHg,but less than 1.0 mmHg (indicated by the group range 0.5≦SW.dP<1). Themidpoint (or mean) of the group is 0.75 [(0.5+1.0)/2]. Since theobservation is categorized or grouped, the midpoint of the group isused. Similarly, the first latency (SW.dT) group is termed 0.1,corresponding to a latency of 0.1 seconds. This latency group includeslatencies with duration greater or equal to 0.10 seconds, but less than0.11 seconds (indicated by the group range 0.10≦SW.dT<0.11). The groupmidpoint is 0.105 [(0.10+0.11)/2]. The matrix distribution is computedduring each individual of said time sequence windows 405 in a continuousseries of said time sequence windows 405 during a recording. In thisparticular example duration of 6 seconds was used. The duration of eachselectable time sequence window should lie in a time range of 5-15seconds, though these durations represent no limitation of the scope ofthe invention.

TABLE 1 A small part of a first matrix showing the single pressure wavedistribution of Time Sequence[360] presented in FIG. 4b. Group name 0.51 1.5 2 2.5 3 3.5 Group range 0.5 ≦ 1.0 ≦ 1.5 ≦ 2.0 ≦ 2.5 ≦ 3.0 ≦ 3.5 ≦SW.dP < 1.0 SW.dP < 1.5 SW.dP < 2.0 SW.dP < 2.5 SW.dP < 3.0 SW.dP < 3.5SW.dP < 4.0 Group midpoint 0.75 1.25 1.75 2.25 2.75 3.25 3.75 0.1 0.10 ≦SW.dT < 0.11 0.105 1 1 0.11 0.11 ≦ SW.dT < 0.12 0.115 1 0.12 0.12 ≦SW.dT < 0.13 0.125 0.13 0.13 ≦ SW.dT < 0.14 0.135 0.14 0.14 ≦ SW.dT <0.15 0.145 0.15 0.15 ≦ SW.dT < 0.16 0.155 0.16 0.16 ≦ SW.dT < 0.17 0.1650.17 0.17 ≦ SW.dT < 0.18 0.175 0.18 0.18 ≦ SW.dT < 0.19 0.185 0.19 0.19≦ SW.dT < 0.20 0.195 0.2 0.20 ≦ SW.dT < 0.21 0.205 0.21 0.21 ≦ SW.dT <0.22 0.215 0.22 0.22 ≦ SW.dT < 0.23 0.225 0.23 0.23 ≦ SW.dT < 0.24 0.2351 0.24 0.24 ≦ SW.dT < 0.25 0.245 0.25 0.25 ≦ SW.dT < 0.26 0.255 0.260.26 ≦ SW.dT < 0.27 0.265 1 0.27 0.27 ≦ SW.dT < 0.28 0.275 0.28 0.28 ≦SW.dT < 0.29 0.285

When computing the mean frequency of a two-dimensional distribution withi rows and j columns, both dimensions must be considered (c=columns;r=rows). The result is the mean value of the distribution when thevalues from both the row and column mean are considered. First, thelatency (SW.dT) mean value (or row mean), with respect to the amplitude(SW.dP) values (columns) is determined (m_(i)). The m_(i) for eachlatency (ΔT) row is determined, by using the equation 77.

$\begin{matrix}{m_{i} = {\sum\limits_{j = 1}^{c}{A_{j}w_{ij}}}} & (77)\end{matrix}$

A_(j) is the i^(th) column midpoint, referring to an amplitude (SW.dP)group value; and w_(ij) is the frequency (count) of the i^(th) SW.dT rowand j^(th) SW.dP column cells.

$\begin{matrix}{{{Row}\mspace{14mu}{mean}} = {{{Mean}({dt})} = \frac{\sum\limits_{i = 1}^{r}{m_{i}B_{i}}}{\sum\limits_{i = 1}^{r}{m\; i}}}} & (78)\end{matrix}$

B_(i) is the i^(th) row SW.dT midpoint value (r=row). The term “i^(th)SW.dT row and j^(th) SW.dP column cell” refers to a matrix cell with thecoordinates “i^(th) row and j^(th) column cell”. Such a cell is found inthe crossing point of a horizontal line drawn through the midpoint ofrow i, and a vertical line drawn through the midpoint of column j. Toillustrate the process, the data of Table I are used to calculate themean row value. Application of the equations (77) and (78) gives a rowmean with respect to columns equal to 0.169 seconds (2.746/16.25). Thecalculations are shown in more detail in Table 2.

TABLE 2 Computation of row [latency (SW.dT)] mean with respect tocolumns [amplitude (SW.dP)]. m_(i) SW.dT_(i) m_(i) × SW.dT_(i) 1 ×2.75 + 1 × 3.25 = 6.0 0.105 0.630 1 × 3.25 = 3.25 0.115 0.374 1 × 3.75 =3.75 0.235 0.881 1 × 3.25 = 3.25 0.265 0.861 Sum = 16.25 2.746 Row mean:2.746/16.25 = 0.169 seconds

Second, the mean (SW.dP) value (columns), with respect to the latency(SW.dT) value (rows), is determined (m_(j)). The column amplitude(SW.dP) mean value are found using the same approach as used for findingthe mean row latency (SW.dT) value. First, the m_(j) for each SW.dPcolumn is found, as given in equation (79).

$\begin{matrix}{m_{j} = {\sum\limits_{i = 1}^{r}{B_{i}w_{ij}}}} & (79)\end{matrix}$

B_(i) is the i^(th) row SW.dT midpoint, referring to a SW.dT group valueand w_(ij) is the frequency for the i^(th) row and j^(th) column.

$\begin{matrix}{{{Column}\mspace{14mu}{mean}} = {{{Mean}({dP})} = \frac{\sum\limits_{j = 1}^{c}{m_{j}A_{j}}}{\sum\limits_{j = 1}^{c}{m\; i}}}} & (80)\end{matrix}$

A_(j) is the j^(th) column SW.dP value midpoint (c=column). Thecalculations are shown in Table 3, using the equations (79) and (80),the column mean with respect to rows will be equal to 3.329 mmHg(2.746/0.825).

TABLE 3 Computation of column [amplitude (SW.dP)] mean with respect torows [latency (SW.dT)]. m_(j) SW.dP_(j) m_(j) × SW.dP_(j) 1 × 0.105 =0.105 2.75 0.289 1 × 0.105 + 1 × 0.115 + 1 × 0.265 = 0.485 3.25 1.576 1× 0.235 = 0.235 3.75 0.881 Sum = 0.825 2.746 Column mean: 2.746/0.825 =3.329 mmHg

Thus, the mean wave for the particular time sequence 405 shown in FIG. 4b (Time Sequence[360]) was 0.17 seconds/3.33 mmHg, the mean intracranialpressure (ICP) wave of this time sequence window 405 had a mean wavelatency (TS.MeanWavedT) of 0.17 seconds (TS[360].MeanWavedT=0.17 sec)and an mean wave amplitude (TS.MeanWavedP) of 3.33 mmHg(TS[360].MeanWavedT=3.33 mmHg).

It is now described the process of computing mean wave rise timecoefficient (TS.MeanWaveRT). This analysis comprises the steps ofcreating a second matrix that is one-dimensional, based on determiningnumber of single pressure waves with pre-selected values related to risetime coefficient (SW.RT), the axis being related to an array ofpre-selected values of rise time coefficient (SW.RT), and whereinindicating for each matrix cell in said second one-dimensional matrix anumber of occurrences of pre-selected rise time coefficients (SW.RT)related to successive measurements of single pressure waves during saidindividual time sequence window. The single pressure wave parameter risetime coefficient (SW.RT) is categorized into groups, said groupsreflecting ranges of said single wave (SW.x)-related parameter values.It is computed balanced position for a number of occurrences of saidsingle pressure wave (SW.x)-related parameter rise time coefficient(SW.RT) in said second matrix, to yield an analysis output. Saidbalanced position of said second matrix of numbers of rise timecoefficient (SW.RT) combinations corresponds to the mean wave rise timecoefficient (TS.MeanWaveRT) of said time sequence window. TS.MeanWaveRTis computed according to equation (81). There are two variables x_(i),and w_(i) where x_(i) is equal to the Group midpoint of each observedSW.RT and w_(i) is equal to the frequency or number of occurrenceswithin a Group range. The Observation value x_(i) is the group midpointof the observed SW.RT.

$\begin{matrix}{{\overset{\_}{X} = \frac{\sum\limits_{i = 1}^{k}{x_{i}w_{i}}}{\sum\limits_{i = 1}^{k}w_{i}}},{k = {{number}\mspace{14mu}{of}\mspace{14mu}{observations}}}} & (81)\end{matrix}$

An example of a second matrix is now shown in Table 4 (the observationsare example values for the purpose of illustrating the concept).

TABLE 4 A second matrix showing a single pressure wave distribution.Group name 4.5 5.0 5.5 6.0 Group range 4.5 ≦ 5.0 ≦ 5.5 ≦ 6.0 ≦ SW.dP <5.0 SW.dP < 5.5 SW.dP < 6.0 SW.dP < 6.5 Group 4.75 5.25 5.75 6.25midpoint Observation 2 1 4

According to equation (81), balanced position of single pressure wavedistribution of rise time coefficient in Table 4 equals:

(2×4.75 mmHg/sec+1×5.25 mHg/sec+4×5.75 mmHg/sec)/7=5.39 mmHg/sec.

Mean amplitude pressure of a time sequence window (TS.MeandP)corresponds to the sum of amplitude (SW.dP) values divided by the numberof individual single pressure waves during said individual time sequencewindow. The mathematical process is further described in equation (21).

Mean latency of a time sequence window (TS.MeandT) corresponds to thesum of latency (SW.dT) values divided by number of individual singlepressure waves during said individual time sequence window. Themathematical process is further described in equation (23).

Mean rise time coefficient of a time sequence window (TS.MeanRT)corresponds to the sum of rise time coefficient (SW.RT) values dividedby the number of individual single pressure waves during said individualtime sequence window. The mathematical process is further described inequation (25).

Absolute mean single wave pressure of a time sequence window(TS.Mean_(SW)P) corresponds to the sum of mean pressure (SW.Mean_(SW)P)values divided by number of individual single pressure waves during saidindividual time sequence window. Mean pressure value for an individualof said single pressure waves (SW.Mean_(SW)P) is the sum of samplevalues during the time of a wave duration, i.e. from starting diastolicminimum pressure (SW.P_(min1)) to ending diastolic minimum pressure(SW.P_(min2))−1 divided by numbers of samples. For example, mean singlewave pressure (TS.Mean_(SW)P) for the time sequence window 405 shown inFIG. 4 b is the average of mean pressure for each of the five includedsingle pressure waves 408 (termed SW[1], SW[2], SW[3], SW[4], andSW[5]), as further detailed in equation (29). Mean pressure for eachincluded single pressure wave (SW.Mean_(SW)P) 408 is computed accordingto equation (9). Mean single wave pressure (TS.Mean_(SW)P) for the timesequence window 405 is the average of mean pressure of the five acceptedsingle pressure waves [TS[360].Mean_(SW)P=(11.87+11.89+11.65+11.56+12.36)/5=11.87 mmHg]. Mean pressurewas 11.87 mmHg for SW[1] (913.99 mmHg/77 samples), 11.89 for SW[2](1034.4 mmHg/87 samples), 11.65 mmHg for SW[3] (1036.85 mmHg/89samples), 11.56 mmHg for SW[4] (971 mmHg/84 samples), and 12.36 forSW[5] (1087.68/88 samples).

Reference is now again given to FIG. 8. During said Computing Step thetime sequence (TS.x)-related parameters 812 are determined on the basisof included time sequence windows 810, which were determined through theIdentifying Steps. Based on the determined time sequence (TS.x)-relatedparameters 812, an analysis output 813 is determined in saidPresentation Step (FIG. 8). Said analysis output 813 can be presented indifferent ways, for example including one or more of said parameters:Mean wave amplitude (TS.MeanWavedP), mean wave latency (TS.MeanWavedT),mean wave rise time coefficient (TS.MeanWaveRT), mean amplitude(TS.MeandP), mean latency (TS.MeandT), mean rise time coefficient(TS.MeanRT), and mean single wave pressure (TS.Mean_(SW)P).

During an ongoing sampling and digitalization of pressure-relatedsignals 801, said analysis output 813 can be presented as numericalvalues of time sequence (TS.x)-related parameters 812 on a display 814for each of said included time sequence windows 810. Given duration ofsaid individual time sequence windows 810 of 6 seconds, the parametervalue is displayed each 6 seconds on said display 814. For example, meanwave amplitude (TS.MeanWavedP) and mean wave latency (TS.MeanWavedT) canbe displayed as the values 6.5 mmHg/0.23 seconds during Time Sequence[n]and as e.g. values 6.7 mmHg/0.24 seconds during Time Sequence[n+1] (thevalues 6.5 mmHg/0.23 seconds and 6.7 mmHg/0.24 seconds are only examplevalues). Thereby, the parameter values are updated each 6 seconds, giventhis particular time window duration.

Another way of presenting the analysis output 813 of one or more of saidtime sequence parameters 812 is creation of histogram 815 distributionof values of said parameters 812. Typically such a histogram 815includes a selectable number of time sequence windows of a signal 801.During an ongoing sampling and digitalization of pressure-relatedsignals 801, said histogram 815 may include all included time sequencewindows 810 that have been included so far. Given that e.g. 700 timesequence windows 810 have been included (i.e. Time Sequence[1] to TimeSequence[700]), the histogram 815 distribution is created based on timesequence (TS.x)-related parameters 812 of 700 included time sequencewindows 810. Given that histogram 815 is created after the end ofsampling of said pressure-related signals 801, the histogram 815distribution is typically created based on time sequence (TS.x)-relatedparameters 812 of the included time sequence windows 810 of said signal801.

Still another way of presenting the analysis output 813 of one or moreof said time sequence parameters 812 is creation of a quantitativematrix 816 of values of said parameters 812. Typically such aquantitative matrix 816 includes a selectable number of time sequencewindows of a signal 801. Said quantitative matrix 816 is created basedon determining numbers of one of said time sequence parameters 812 withpre-selected values of said parameters 812, wherein one axis of thequantitative matrix 816 is related to an array of pre-selected values ofsaid parameter 812, wherein the other axis is related to an array ofpre-selected numbers of consecutive included time sequence windows 810,and wherein indicating for each matrix cell at respective intersectionsin said quantitative matrix 816 a number of occurrence of matchesbetween a specific parameter 812 value and a specific number of includedtime sequence windows 810. The parameter 812 values are categorized intogroups, said groups reflecting ranges of said parameter 812 values.Furthermore, the occurrence of matches in said quantitative matrix 816is indicated through actual number or standardisation based number ofmatches during a specific measurement period, said standardisation basednumber of matches being a function of the length of the specificmeasurement period. A measurement period refers to given duration ofsaid signal 801. A pressure signal 801 refers to a number of sequentialand available pressure-related samples during a time period, whereineach of said time-related sequential samples can be referenced by asample number and elapsed time determined by sample location number andsample frequency.

To further illustrate the creation of a quantitative matrix 816, aspecific example is now presented, though this example is not intendedto limit the scope of the invention.

Given a continuous intracranial pressure (ICP) signal 801 of 6 hoursduration including a total of 3000 included, i.e. accepted, timesequence windows 810, there is allowed computation of time sequence(TS.x)-related parameters 812 of said 3000 included time sequencewindows 810 (Time Sequence[1] to Time Sequence[3000]). In this exampleit is focused on mean wave amplitude (TS.MeanWavedP). The quantitativematrix shown in Table 5 is based on all the 3000 included time sequencewindows 810 and corresponding 3000 (TS.MeanWavedP) parameter values. Asindicated in Table 5, said quantitative matrix is created based ondetermining numbers of parameters 812 with pre-selected values ofTS.MeanWavedP 812. The one axis of the quantitative matrix is related toan array of pre-selected values of TS.MeanWavedP 812 (in this examplethe pre-selected values were 4.0 mmHg, 5.0 mmHg, and 6.0 mmHg, referredto as group ranges). The parameter 812 values are categorized intogroups, said groups reflecting ranges of said parameter 812 values:Amplitude Group 4: 4.0 mmHg≦TS.MeanWavedP <5.0 mmHg. Amplitude Group 5:5.0 mmHg≦TS.MeanWavedP <6.0 mmHg. Amplitude Group 6: 6.0mmHg≦TS.MeanWavedP <7.0 mmHg. The other axis is related to an array ofpre-selected numbers of included time sequence windows 810 (in thisexample the pre-selected numbers were 5, 10 and 20). Since the durationof each of said individual time sequence windows is known (6 seconds inthis example), this other axis also is related to pre-selected durations(in this example 30 seconds, 60 seconds, and 120 seconds): Time SequenceNumber Group 5: 0<N≦5 (corresponding to Time Group 0<Time ≦30). TimeSequence Number Group 10: 5<N≦10 (corresponding to Time Group 30<Time≦60). Time Sequence Group 20: 10<N≦20 (corresponding to Time Group60<Time ≦120). For each matrix cell is indicated at respectiveintersections in said quantitative matrix a number of occurrence ofmatches between a specific parameter (TS.MeanWavedP) 812 value and aspecific number of included time sequence windows 810. The occurrence ofmatches in this matrix (Table 5) is indicated through actual number ofmatches during the specific measurement period of 6 hours. For example,according to this matrix distribution, mean wave amplitude(TS.MeanWavedP) values 812 equal to or larger than 5.0 mmHg but lessthan 6.0 mmHg occurring in five up to ten consecutive included timesequence windows 810 (5<N≦10) occurred two times during the recording of6 hours including a total of 3000 included time sequence windows 810. InTable 6 is presented an identical quantitative matrix as in Table 5,though in this quantitative matrix (Table 6) the occurrence of matchesis indicated through standardisation-based number of matches being afunction of the length of the specific measurement period. Theoccurrence was standardised to a length of a recording period of 1 hour.Thereby each number is said matrix cell was multiplied with a factorequal to ⅙ in this specific example.

TABLE 5 A quantitative matrix of pre-selected combinations ofTS.MeanWavedP 812 parameter values and number of included time sequencewindows 810 (actual numbers presented in each matrix cell). Group name 45 6 Group ranges 4.0 ≦ DP < 5.0 5.0 ≦ DP < 6.0 6.0 ≦ DP < 7.0 5 0 < N ≦5 0 < Time ≦ 30 8 5 2 10  5 < N ≦ 10 30 < Time ≦ 60  6 2 1 20 10 < N ≦20 60 < Time ≦ 120 3 1

DP refers to TS.MeanWavedP group range; N refers to number of includedtime sequence windows; and Time refers to the time of corresponding timesequences windows.

According to this quantitative matrix (Table 5), within said signalcontaining 3000 included time sequence windows there were fiveoccurrences for which up to five consecutive time sequence windows had atime sequence parameter TS.MeanWavedP between 5.0 and up to 6.0 mmHg(5.0≦DP<6.0; the number 5 is highlighted in bold type; Table 5).

TABLE 6 A quantitative matrix of pre-selected combinations ofTS.MeanWavedP 812 parameter values and number of included time sequencewindows 810 (standardization based numbers presented in each matrixcell). Group name 4 5 6 Group ranges 4.0 ≦ DP < 5.0 5.0 ≦ DP < 6.0 6.0 ≦DP < 7.0 5 0 < N ≦ 5 0 < Time ≦ 30 1.3 (=8 × ⅙) 0.8 (=5 × ⅙) 0.3 (=2 ×⅙) 10  5 < N ≦ 10 30 < Time ≦ 60    1 (=6 × ⅙) 0.3 (=2 × ⅙) 0.2 (=1 × ⅙)20 10 < N ≦ 0 60 < Time ≦ 120 0.5 (=3 × ⅙) 0.2 (=1 × ⅙)

DP refers to TS.MeanWavedP group range; N refers to number of includedtime sequence windows; and Time refers to the time of corresponding timesequences windows.

In test recordings the quantitative matrix has been shown to be of greatclinical significance, as related to several causes. It may be somewhatdifficult to assess a continuous pressure recording of several hoursduration since time sequence (TS.x)-related parameters may sometimeschange over time. Said quantitative matrix may be used to get a usefulsummary of the recording. In particular, said quantitative matrix isuseful for comparisons of continuous pressure recordings betweendifferent individuals. Test recordings including computation ofquantitative matrixes of TS.MeanWavedP and TS.MeandP have been found tobe of great value. The opportunity to standardize the quantitativematrix is particularly useful for comparisons of pressure recordingsbetween individuals. Thereby, this inventive aspect further enhance thediagnostic value of pressure monitoring,

Finally, the analysis output 813 of one or more of said time sequenceparameters 812 may be presented in various other ways though specificexamples are not presented in this context.

Concerning the method for processing continuous pressure-related signalsderivable from locations inside or outside a human or animal body orbody cavity as summarized in FIG. 8, some examples are given concerningthe usefulness of the method.

For continuous intracranial pressure (ICP) signals 801, the method ofcomputing time sequence (TS.x)-related parameters 812 such as mean waveamplitude (TS.MeanWavedP), mean amplitude (TS.MeandP), mean wave latency(TS.MeanWavedT), and mean latency (TS.MeandT) is useful for determiningintracranial compliance (i.e. the inverse of intracranial elastanse).Determination of intracranial compliance is one of the major reasons forintracranial pressure (ICP) monitoring, though existing and currentlyused methods in no reliable way reveal intracranial compliance. Thus,the inventive method provides information within the pressure signal 801that is not revealed by existing and currently used methods.

Using currently used, prior art technology of pressure monitoring, wrongcalibration against atmospheric zero pressure level may give misleadingresults. Drift of zero pressure level is another great problem relatedto existing and currently used pressure monitoring. During long-termpressure monitoring, drift of zero pressure level may give misleadingpressure measurements. Since the time sequence (TS.x)-related parameters812 are relative values, errors related to wrong zero pressure level ordrift in zero pressure level is eliminated by this invention.

By existing and currently used pressure monitoring technology, there islimited ways of controlling the quality of the pressure-related signals.A misleading pressure measurement may be made given wrong placement ofthe pressure sensor. There is minimal opportunity to control whether thepressure measurements are related to the cardiac beat induced pressures.By this invention the time sequence (TS.x)-related parameters 812 areonly computed for included time sequence windows 810, including includedsingle pressure waves 807. This approach gives an opportunity to controlthe quality of the pressure measurements.

Reference is now given to the third feature of this invention, which isfurther detailed in independent claim 1 and subsequent sub-claims 2-25.This third feature of the invention is particularly illustrated in FIG.9, as well as in FIGS. 7, 10 and 11.

Said third feature of the invention relates to a method for processingtwo or more simultaneous continuous pressure-related signals derivablefrom a human or animal body from one or more locations thereof electablefrom: inside the body, outside the body, inside body cavity, outsidebody cavity, comprising the steps of obtaining samples of said signalsat specific intervals, and converting thus sampled pressure signals intopressure-related digital data with identical time reference, wherein forselectable and simultaneous time sequence windows the method comprisesthe further steps of identifying from said digital data signal singlepressure waves related to cardiac beat-induced pressure waves withinsaid two or more simultaneous signals constituting a pressure recording,and identifying from said digital data signal pressure waves related toartifacts or a combination of artifacts and cardiac beat-inducedpressure waves within said two or more simultaneous signals constitutinga pressure recording, and computing time sequence (TS.x)-relatedparameters of said single pressure waves during said identical timesequence windows within said two or more simultaneous signalsconstituting a pressure recording. The method comprises the furthersteps of determining relationships between time sequence (TS.x)-relatedparameters of said identical time sequence windows within said two ormore simultaneous signals constituting a pressure recording, saidrelationships calculated as related time sequence (rTS.x) parameters,and determining said related time sequence (rTS.x) parameters forindividual recordings or a population of recordings. Said related timesequence (rTS.x) parameters are further used for formula-basedadjustment of time sequence windows of individual pressure-relatedsignals, and for creating factorized time sequence (fTS.x) parameters ofsaid individual time sequence windows of said individual continuouspressure-related signal.

Some remarks are required concerning simultaneously sampledpressure-related signals. With reference to FIG. 1, the notation:“Recording[1].Signal[m].Type.Location” denotes a specific Location 113within a specific Type 104 within a specific Signal [n] 102, within aspecific Recording [l] 101. The notation “Recording [l].Signal[m].Type.Sensor” denotes a specific Sensor 112 within a specific Type104 within a specific Signal [n] 102, within a specific Recording [l]101.

The present invention relates to a method for processing most types ofcontinuous pressure-related signals derivable from human beings oranimals, independent of pressure measurement locations and/or sensortype, and whether said sensor is placed inside or outside a human oranimal body or a body cavity. Said method for processing continuouspressure-related signals is also independent of starting points of saidcontinuous pressure-related signals. Furthermore, said method forprocessing continuous pressure-related signals is independent on type ofpressure sensor, said sensor being placed inside or outside a human oranimal body and/or body cavity.

Reference is now given to FIG. 9, wherein an overview of said method foranalyzing two or more simultaneous continuous pressure-related signalsis shown. From a recording 901 (Recording[1]) is analyzed twosimultaneous signals, Signal[1] 902 and Signal[2] 903. Samples areobtained from each respective one of said pressure related signalsSignal[1] 902 and Signal[2] 903, each such sample containing a pressurevalue at a specific time, and wherein said two or more pressure-relatedsignals 902, 903 are all sampled simultaneously. Each of said sampledpressure signals 902, 903 refers to a number of sequential and availablepressure samples during a time period. Selectable and simultaneous timesequence windows are a selected time frame of a sampled signal. Each ofsuch simultaneous selectable time sequence windows is a function of anumber of time-related sequential samples, each individual samplereferenced by a sample number and elapsed time determined by samplelocation number and sample frequency. Concerning notation related torecording 901 and signals 902 and 903, it is referred to FIG. 1. Inshort, a recording 901 is one or more simultaneous signals (e.g.Signal[1] 902 and Signal[2] 903) derivable from locations inside oroutside a human or animal body or body cavity, each of said signalshaving identical time reference, though it is not a requirement that thestart time is identical for all signals of a recording. Attributes ofeach signal include type, frequency, and actual samples. As indicatedwith reference to FIG. 1, a signal is equivalent to: Recording[l].Signal [m].Samples [n], wherein each of said samples contains apressure value at a specific time.

As indicated in FIG. 9, the process method incorporates differentIdentifying Steps, Computing Step, Determining and Application Step.Said Identifying Steps include identification of all separate peaks andvalleys 904, 905 in said simultaneously sampled signals (Signal[1] 902and Signal[2] 903) that constitute a pressure recording (Recording[1]901). Each of said peaks is a sample with a pressure value and a timestamp or location, and each of said valleys is a sample with a pressurevalue and a time stamp or location.

The result of applying signal specific General Methods Criteria 906, 907is either included peak/valley pair combinations 908, 909 or excludedpeak/valley pair combinations 910, 911 in said simultaneous signals(Signal[1] 902, Signal[2] 903) constituting a pressure recording(Recording[1] 901).

After applying signal specific Single Wave & Delta Single Wave Criteria912, 913 to said included peak/valley pairs 908, 909, the output iseither included single pressure waves 914, 915 or excluded pressurewaves 916, 917 in said simultaneous signals (Signal[1] 902, Signal[2]903), constituting a pressure recording (Recording[1] 901). Saidcriteria 912, 913 relate to thresholds and ranges of single pressurewave (SW.x)-related parameters and delta single pressure wave(ASW.x)-related parameters during time sequence windows. After applyingthe Single Wave & Delta Single Wave Criteria 912, 913 to included paircombinations of peak/valley pairs 908, 909 in said simultaneous signals(Signal[1] 902, Signal[2] 903), said pairs 908, 909 correspond toincluded single pressure waves 914, 915 in said simultaneous signals(Signal[1] 902, Signal[2] 903) constituting a pressure recording(Recording[1] 901). Pair combinations of diastolic minimum pressure(SW.P_(min1)) and systolic maximum pressure (SW.P_(max)) characterizesingle pressure waves created by cardiac beat-induced pressure waves.Said criteria 912, 913 exclude for further analysis pressure waves 916,917 (i.e. minimum-maximum pressure (SW.P_(min1)/SW.P_(max)) pairs)during said time sequence windows with said single pressure wave (SW.x)-and delta single pressure wave (ΔSW.x)-related parameters outsideselected criteria 912, 913 for thresholds and ranges of said parameters.Said criteria 912, 913 include for further analysis single pressurewaves 914, 915 having single pressure wave (SW.x)- and delta singlepressure wave (ΔSW.x)-related parameters within selected criteria 912,913 for thresholds and ranges of said single pressure wave(SW.x)-related parameters. Pair combinations of diastolic minimumpressure (SW.P_(min1)) and systolic maximum pressure (SW.P_(max))correspond to the diastolic minimum pressures and systolic maximumpressures of individual of pressure waves created by each of saidcardiac beats.

Signal-specific Time Sequence & Delta Time Sequence Criteria 918, 919are applied to each of said time sequence windows in a continuous seriesof time sequence windows of said simultaneous signals (Signal[1] 902,Signal[2] 903) constituting a pressure recording (Recording[1] 901).Said criteria 918, 919 for thresholds and ranges of said time sequence(TS.x) and delta time sequence (ΔTS.x)-related parameters determineincluded time sequence windows 920, 921 and excluded time sequencewindows 922, 923 of said simultaneous signals (Signal[1] 902, Signal[2]903) constituting a pressure recording (Recording[1] 901). Said criteria918, 919 exclude for further analysis time sequence windows 922, 923with time sequence (TS.x)- and delta time sequence (ΔTS.x)-relatedparameters outside selected criteria 918, 919 for thresholds and rangesof said parameters. Said criteria 918, 919 include for further analysistime sequence windows 920, 921 having time sequence (TS.x) and deltatime sequence (ΔTS.x)-related parameters within selected criteria 918,919 for thresholds and ranges of said time sequence (TS.x) and deltatime sequence (ΔTS.x)-related parameters.

It should be understood that said General Methods Criteria 906, 907,Single Wave & Delta Single Wave Criteria 912, 913 and Time Sequence &Delta Time Sequence Criteria 918, 919 are signal-specific andlocation-specific, meaning that said criteria are different fordifferent types of signals and locations in said signals. In a nextComputing Step (FIG. 9) is computed time sequence (TS.x)-relatedparameters 924, 925 for each individual of said included time sequencewindows 920, 921 of said simultaneous signals (Signal[1] 902, Signal[2]903) constituting a pressure recording (Recording[1] 901).

The Identifying Steps are applied to each of said time sequence windowsin a continuous series of said time sequence windows during a recording.

The Computing Step is applied to each of said included time sequencewindows 920, 921 in a continuous series of said time sequence windowsduring a recording.

In a final Determining Step, there is determined relationships betweentime sequence (TS.x)-related parameters 924, 925 of said identicalincluded time sequence windows 920, 921 within said simultaneous signals(Signal[1] 902, Signal[2] 903) constituting a pressure recording(Recording[1] 901). Said relationships are calculated as related timesequence (rTS.x) parameters 926, determined for individual recordings,as well as for a population of recordings. Said related time sequence(rTS.x) parameters 926 can be constant relationships and/orformula-based relationships between identical time sequence(TS.x)-related parameters 924, 925 of different pressure signals(Signal[1] 902, Signal[2] 903) with identical time reference. Saidrelated time sequence (rTS.x) parameters are computed for eachindividual of said included time sequence windows 920, 921 in acontinuous series of time sequence windows of said signals (Signal[1]902, Signal[2] 903). In addition, for all included time sequence windows920, 921 in said signals (Signal[1] 902, Signal[2] 903), the mean valueof such parameters 926 is determined. For a population of recordings,population-based formulas for said related time sequence (rTS.x)-relatedparameters 926 are determined. Based on said related time sequence(rTS.x) parameters 926, formula-based adjustments of time sequences ofindividual pressure-related signals 927 are made possible. Thereby,factorized time sequence (fTS.x) parameters of individual time sequencewindows of individual continuous pressure-related signal are createdfrom said formula-based adjustments 927. Factorized time sequence(fTS.x) parameters are derived from related time sequence (rTS.x) 926values together with time sequences of the signal being factorized.

Concerning the Identifying Steps, the method is comparable to theprocess described for FIGS. 2, 3 a, 3 b, 3 c, 4 a, 4 b, 5 a, 5 b, 6, and7 a, but with reference to FIG. 9 the method is applied to severalsimultaneous signals.

Details about General Methods Criteria 906, 907, Single Wave & DeltaWave Criteria 912, 913 and Time Sequence & Delta Time Sequence Criteria918, 919 are already commented on with reference to FIGS. 2 and 8, andthese aspects are therefore not commented on further in the context ofFIG. 9.

With reference to FIG. 9 it is made clear that said criteria aresignal-specific and location specific being different for differentsignal types and locations of a signal.

Computation of related time sequence (rTS.x) parameters can be selectedfrom the group of

-   -   relationship of mean values of starting diastolic minimum        pressure of two or more perfect time sequence windows from two        or more different pressure signals (rTS.P_(min1)),    -   relationship of standard deviation of mean values of starting        diastolic minimum pressure of two or more perfect time sequence        windows from two or more different pressure signals        (rTS.MeanP_(min1) _(—) STD),    -   relationship of mean values of systolic maximum pressure of two        or more perfect time sequence windows from two or more different        pressure signals (rTS.MeanP_(max)),    -   relationship of standard deviation of mean values of systolic        maximum pressure of two or more perfect time sequence windows        from two or more different pressure signals (rTS.MeanP_(max)        _(—) STD),    -   relationship of mean amplitude values of two or more perfect        time sequence windows from two or more different pressure        signals (rTS.MeandP),    -   relationship of standard deviation of mean amplitude of two or        more perfect time sequence windows from two or more different        pressure signals (rTS.MeandP_STD),    -   relationship of mean latency of two or more perfect time        sequence windows from two or more different pressure signals        (rTS.MeandT),    -   relationship of standard deviation of mean latency of two or        more perfect time sequence windows from two or more two        different pressure signals (rTS.MeandT_STD),    -   relationship of mean rise time coefficient of two or more        perfect time sequence windows from two or more different        pressure signals (rTS.MeanRT),    -   relationship of standard deviation of mean rise time coefficient        of two or more perfect time sequence windows from two or more        different pressure signals (rTS.MeanRT_STD),    -   relationship of mean wave duration of two or more perfect time        sequence windows from two or more different pressure signals        (rTS.MeanWD),    -   relationship of standard deviation of mean wave duration of two        or more perfect time sequence windows from two or more different        pressure signals (rTS.MeanWD_STD),    -   relationship of mean single wave pressure of two or more perfect        time sequence windows from two or more different pressure        signals (rTS.Mean_(SW)P),    -   relationship of standard deviation of mean single wave pressure        of two or more perfect time sequence windows from two or more        different pressure signals (rTS.Mean_(SW)P_STD),    -   relationship of mean diastolic minimum pressure difference of        two or more perfect time sequence windows from two or more        different pressure signals (rTS.MeanDiff_P_(min)),    -   relationship of standard deviation of mean diastolic minimum        pressure difference of two or more perfect time sequence windows        from two or more different pressure signals        (rTS.MeanDiff_P_(min) _(—) STD),    -   relationship of mean systolic maximum pressure difference of two        or more perfect time sequence windows from two or more different        pressure signals (rTS.MeanDiff_P_(max)),    -   relationship of standard deviation of mean systolic maximum        pressure difference of two or more perfect time sequence windows        from two or more different pressure signals        (rTS.MeanDiff_P_(max) _(—) STD),    -   relationship of mean amplitude difference of two or more perfect        time sequence windows from two or more different pressure        signals (rTS.MeanDiff_dP),    -   relationship of standard deviation of mean amplitude difference        of two or more perfect time sequence windows from two or more        different pressure signals (rTS.MeanDiff_dP_STD),    -   relationship of mean latency difference of two or more perfect        time sequence windows from two or more different pressure        signals (rTS.MeanDiff_dT),    -   relationship of standard deviation of mean latency difference of        two or more perfect time sequence windows from two or more        different pressure signals (rTS.MeanDiff_dT_STD),    -   relationship of mean rise time coefficient difference of two or        more perfect time sequence windows from two or more different        pressure signals (rTS.MeanDiff_RT),    -   relationship of standard deviation of mean rise time coefficient        difference of two or more perfect time sequence windows from two        or more different pressure signals (rTS.MeanDiff_RT_STD),    -   relationship of mean wave duration difference of two or more        perfect time sequence windows from two or more different        pressure signals (rTS.MeanDiff_WD),    -   relationship of standard deviation of mean wave duration        difference of two or more perfect time sequence windows from two        or more two different pressure signals (rTS.MeanDiff_WD_STD),    -   relationship of mean single wave pressure difference of two or        more perfect time sequence windows from two or more different        pressure signals (rTS.MeanDiff_Mean_(SW)P),    -   relationship of standard deviation of mean single wave pressure        difference of two or more perfect time sequence windows from two        or more two different pressure signals        (rTS.MeanDiff_Mean_(SW)P_STD),    -   relationship of single wave count of two or more perfect time        sequence windows from two or more different pressure signals        (rTS.SWCount),    -   relationship of mean wave amplitude of two or more perfect time        sequence windows from two or more different pressure signals        (rTS.MeanWavedP),    -   relationship of mean wave latency of two or more perfect time        sequence windows from two or more different pressure signals        (rTS.MeanWavedT), and    -   relationship of mean wave rise time coefficient of two or more        perfect time sequence windows from two or more different        pressure signals (rTS.MeanWaveRT).

The term “perfect” in this context implies the term “accepted” afterapplication of very strict criteria.

It should be noted that said simultaneous continuous pressure-relatedsignals (Signal[1] and Signal[2]) include two dimensions, a pressurescale and a time scale. The related time sequence (rTS.x) parameterswhich describe the relationships between two signals are based onobservations between identical time sequences from said signals(Signal[1].TS[x] and Signal[2].TS[x]), where x denotes a specificlocation and time within the same recording (Recording[1]). Thus, therelated time sequence (rTS.x) parameters establish relationships forboth the pressure and time dimensions of a continuous signal.

In test recordings, related time sequence (rTS.x) parameters founduseful for comparing pressure-related characteristics betweensimultaneous signals include such as, e.g.: rTS.MeandP, rTS.MeandP_STD,rTS.MeanRT, and rTS.MeanWavedP. Parameters found useful for comparisonsof time related characteristics include such as, e.g.: rTS.MeandT,rTS.MeanWD, rTS.MeanWD_STD, rTS.MeanDiff_dT, and rTS.MeanDiff_WD.Comparisons of rTS.x values between different time sequence parametersmay be even more powerful in determining relationships.

In general, it should be noted that the sensor element itself wherefromthe pressure signals are derived, could be located both within andoutside the body cavity. The type of sensor does not represent alimitation of the scope of the present invention. For example,intra-dural pressure signals may be derived from a fluid catheter placedvia a cannula within the intra-dural compartment, wherein the sensorelement can be placed outside the body thus deriving pressure signalsfrom the catheter fluid outside the body and in distance from thelocation per se (i.e. the intra-dural compartment). In anothersituation, a sensor element itself is located on the catheter tip thatis placed within the intra-dural compartment. The situation is similarfor arterial blood pressure (ABP) monitoring, when a catheter is placedvia an intra-arterial cannula, and the sensor element is either outsidethe body measuring pressures within the distal part of the catheter orthe sensor element is placed on the proximal tip of the catheter placedwithin the intra-arterial compartment. Therefore, the sensor type andsensor location represents no limitation of the scope of the invention.

Continuous simultaneous signals within a recording may be derivable froma human or animal body from one or more locations thereof electablefrom: inside the body, outside the body, inside body cavity, outsidebody cavity. Reference is now given to FIG. 9 showing two differentsignals (Signal[1] 902, Signal[2] 903), and examples are now givenconcerning simultaneous pressure-related signals from differentlocations or pressure sources. Said simultaneously sampled signals canbe obtained from two simultaneous continuous intracranial intra-duraland epidural pressure-related signals. In this situation thesimultaneous signals constituting a recording are derived simultaneouslyfrom two different locations: Inside the dura mater (i.e. intra-dural)and outside the dura mater (i.e. epidural). For example, thecorresponding notations can be: Recording[60].Signal[1].ICP.Intra-dural;and Recording[60].Signal[2].ICP.Epidural. These different extra- andintra-dural compartments can in a way be considered as different bodycavities though both the extra- and intra-dural body cavities arelocated within the intracranial compartment and are measuring from thesame pressure origin.

Continuous signals can be obtained from at least two simultaneouscontinuous intracranial intradural and intraspinal intra-duralcerebrospinal fluid pressure signals. For example, said intraduralpressure-related signals may be derived from a catheter placed withinthe cerebral ventricles, or a sensor placed within the brain parenchymaor within the subdural extra-cerebral compartment. The sensor elementmay be within the intra-dural compartment or outside the body measuringfluid pressure within said catheter. Said intraspinal intra-duralcerebrospinal fluid pressure signals may as well be derived from afluid-filled catheter with the sensor on the outside of the body or viaa sensor element introduced to the intra-spinal and intra-duralcompartment. Intraspinal intra-dural cerebrospinal fluid pressuresignals are obtained during so-called infusion tests.

In another situation, said sampled signals are obtained from at leasttwo simultaneous continuous intracranial intra-dural and extra-cranialpressure signals indicative of intracranial pressure signals. Saidextra-cranial pressure signals indicative of intracranial pressuresignals can be derived from various sources. Some examples are giventhough these examples are not intended to limit the scope of theinvention:

-   a) Firstly, said extra-cranial pressure signals can be related to    air pressure signals derivable from within a human or animal    outlet-sealed outer ear channel. An open and air-filled catheter is    placed within the outer ear channel after airtight closing of the    outer ear channel, thus allowing sampling of pressure-related    signals from the outer airtight outer ear channel. A detailed    description of this concept is given separately.-   b) Secondly, said extra-cranial pressure signals can also be    transcranial Doppler signals, that are transformable into    pressure-related signals indicative of intracranial pressure    signals. Transcranial Doppler measures arterial blood flow signals    that are transformable into pressure-related signals indicative of    intracranial pressure signals.-   c) Thirdly, said extra-cranial pressure signals can be cranial    impedance-related signals, being transformable into pressure-related    signals indicative of intracranial pressure signals.-   d) Fourthly, said extra-cranial pressure signals are fontanel    applanation pressure signals, being transformable into    pressure-related signals indicative of intracranial pressure    signals. This strategy is particularly useful in children below 1-2    years of age with a non-closed fontanel.-   e) Fifthly, said extra-cranial pressure signals are ocular    applanation pressure signals.

Said simultaneous pressure samples derived from intracranial intra-duraland extra-cranial pressure signals indicative of intracranial pressuresignals may as well be combined with outer simultaneous signalsconstituting a pressure recording. Such signal may be arterial bloodpressure (ABP) signals or electrocardiogram (ECG) signals.

Simultaneous pressure-related signals may be sampled from at least twosimultaneous continuous intra-arterial and extra-arterial pressuresignals indicative of intra-arterial pressure signals. Saidextra-arterial pressure signals indicative of intra-arterial pressuresignals can be derived from various sources. Some examples can be giventhough these examples are not intended to limit the scope of theinvention: Firstly, said extra-arterial pressure signals are arterialapplanation pressure signals, being transformable into pressure-relatedsignals indicative of intra-arterial pressure signals. Secondly, saidextra-arterial pressure signals are pulse oxymetry signals, beingtransformable into pressure-related signals indicative of intra-arterialpressure signals. Thirdly, said extra-arterial pressure signals are anyphysiological signals, being transformable into pressure-related signalsindicative of intra-arterial pressure signals.

The significance of the procedure described in FIG. 9 has been shown fordifferent types of test recordings. By using the method described inFIG. 9 it became possible to obtain nearly identical time sequence(TS.x) parameters (e.g. TS.MeandP, TS.MeanWavedP), independent whetherthe pressure sensor was placed within the epidural space or intra-dural.Thus, this feature of the invention makes it possible to measureintracranial pressure (ICP) by placing a sensor within the epiduralspace instead of within the brain tissue itself (intra-dural). Thisrepresents a major advantage since epidural placement of a sensor isless invasive with minimal opportunity of damaging the brain. Thisaspect of the invention is further commented on with reference to FIGS.7 a and 7 b.

In test recordings using the method described in FIG. 9, it also waspossible to compute nearly identical time sequence (TS.x) parameterswhether location of intracranial pressure (ICP) signals was intra-duralwithin the brain parenchyma or within the spinal (lumbar) cerebrospinalfluid (CSF) cavity. Thus, it was possible to precisely measureintracranial pressure (ICP) by placing a cannula within the lumbarcerebrospinal fluid (CSF) cavity. This represents a great advantage,since the procedure is less invasive.

In test recordings it was also possible to obtain nearly identical timesequence (TS.x) parameters by a sensor placed within the outer earchannel as by placing the sensor intra-durally within the brainparenchyma. Aspects of this procedure are further described in FIGS. 10and 11. Obviously it will be a great advantage to be able to measureintracranial pressure (ICP) by placing a sensor within the outer earchannel rather than by placing a sensor within the brain parenchyma orwithin the epidural space.

These examples are not intended to limit the scope of the invention. Amajor application is within the field of arterial blood pressure (ABP)monitoring, without placing a sensor within the blood vessel itself.Nevertheless, test recordings show that the method described inconnection with FIG. 9 provides for a strategy of measuring pressuresinside a body or body cavity by placing the sensor outside said body orbody cavity.

Reference is now given to FIGS. 7 a and 7 b, to further illustrate theconcept of computing related time sequence (rTS.x) parameters on thebasis of time sequence (TS.x)-related parameters of simultaneous signals(corresponding to related time sequence parameters 926 and time sequenceparameters 924, 925 of Signal[1] 902 and Signal[2] 903; FIG. 9). In FIG.7 a is shown a continuous intracranial intra-dural pressure (ICP) signal701 derived from a sensor within the brain parenchyma (Signal[1] 701)(Recording[62].Signal[1].ICP.Intra-dural), and in FIG. 7 b is shown acontinuous intracranial epidural pressure (ICP) signal 702 derived froma sensor within the epidural space (Signal[2] 702)(Recording[62].Signal[2].ICP.Epidural). Thus, both signals Signal[1] 902and Signal[2] 903 were from the same recording (Recording[62]), and weresampled simultaneously with identical time reference, thus withidentical time scale 703 for both Signal[1] 701 and Signal[2] 702. Thepressure scale 704 of Signal[1] 701 (FIG. 7 a) and the pressure scale705 of Signal[2] 702 (FIG. 7 b) had identical resolution though theabsolute pressure levels were different. For both signals are shown twosubsequent time sequence windows, termed Time Sequence[30] 706 (n−1) andTime Sequence[31] 707 (n). These were included time sequence windows(see FIG. 9). For Signal[1] 701 the amplitudes (SW.dP) of the includedsingle pressure waves 708 within the first time sequence window (TimeSequence[30] 706) are numbered 709 (SW[1].dP), 710 (SW[2].dP), 711(SW[3].dP), 712 (SW[4].dP), 713 (SW[5].dP), and 714 (SW[6].dP). ForSignal[1] 701 the amplitudes (SW.dP) of the seven included singlepressure waves 708 within the second time sequence window (TimeSequence[31] 707) are numbered 715 (SW[1].dP), 716 (SW[2].dP), 717(SW[3].dP), 718 (SW[4].dP), 719 (SW[5].dP), 720 (SW[6].dP) and 721(SW[7].dP). For Signal[2] 702 the amplitudes (SW.dP) of the six includedsingle pressure waves 708 within the first time sequence window (TimeSequence[30] 706) are numbered 722 (SW[1].dP), 723 (SW[2].dP), 724(SW[3].dP), 725 (SW[4].dP), 726 (SW[5].dP), and 727 (SW[6].dP). ForSignal[2] 702 the amplitudes (SW.dP) of the seven included singlepressure waves 708 within the second time sequence window (TimeSequence[31] 707) are numbered 728 (SW[1].dP), 729 (SW[2].dP), 730(SW[3].dP), 731 (SW[4].dP), 732 (SW[5].dP), 733 (SW[6].dP) and 734(SW[7].dP).

FIGS. 7 a and 7 b show two simultaneous continuous pressure-relatedsignals 701, 702 derived from different locations that are convertedinto pressure-related digital data with identical time reference 703.For each of said simultaneous time sequence windows (either TimeSequence[30] 706 or Time Sequence[31] 707) are identified the singlepressure waves related to cardiac beat-induced pressure waves withinsaid two or more simultaneous signals (Signal[1] 701 and Signal[2] 702)of a pressure recording. Some of said time sequence (TS.x)-relatedparameters of said single pressure waves 708 during said identical timesequence windows (either Time Sequence[30] 706 or Time Sequence[31] 707)within said two simultaneous signals (Signal[1] 701 and Signal[2] 702)of a pressure recording, are listed in Table 7.

TABLE 7 Data related to time sequence windows shown in FIGs. 7a and 7b.Time sequence (TS.x)-related parameters TS.MeanWavedP TS.MeanWavedTTS.MeandP TS.MeandT Time Sequence[30] Signal[1] 5.0 mmHg 0.265 sec 5.5mmHg 0.265 sec Signal[2] 5.9 mmHg 0.275 sec 5.7 mmHg 0.275 sec TimeSequence[31] Signal[1] 5.1 mmHg 0.268 sec 5.2 mmHg 0.268 sec Signal[2]5.9 mmHg 0.276 sec 6.0 mmHg 0.276 sec Related time sequence (rTS.x)parameter values rTS.MeanWavedP rTS.MeanWavedT rTS.MeandP rTS.MeandTTime Sequence[30] 0.85 0.96 0.96 0.96 (=5.0/5.9) (=0.265/0.275)(=5.5/5.7) (=0.265/0.275) Time Sequence[31] 0.86 0.97 0.87 0.97(=5.1/5.9) (=0.268/0.276) (=5.2/6.0) (=0.268/0.276)

With reference to Table 7, some comments should be made: Firstly, itshould be noted that the time sequence (TS.x)-related parametersTS.MeanWavedP and TS.MeandP were almost identical, and also the timesequence (TS.x)-related parameters TS.MeanWavedT and TS.MeandT wereidentical. Secondly, the various time sequence (TS.x)-related parameterschanged marginally between Time Sequence[30] and Time Sequence[31].Thirdly, the related time sequence (rTS.x) parameters were constantrelationships between said time sequence (TS.x)-related parameters.These two simultaneous time sequence windows (Time Sequence[30] 706 orTime Sequence[31] 707) were selected from a group of 3100 included timesequence windows (Time Sequence[1] to Time Sequence[3100]) of arecording with originally 3600 time sequence windows within each of saidtwo signals (Signal[1] and Signal[2]) constituting the recording(Recording[62]).

To explain in more detail the results shown in Table 7 and FIG. 7, thisspecific example is now explained with reference to FIG. 9 whereinreference numbers to FIG. 9 is given in parenthesis. This is done inorder to further clarify the flow chart of FIG. 9. The recording shownin FIGS. 7 a and 7 b Recording[62] (901) included two signals:Recording[62].Signal[1].ICP.Intra-dural (902);Recording[62].Signal[2].ICP.Epidural (903). Within each of said signals(902, 903) were a total of 3600 time sequence windows. After theIdentifying Steps indicated in FIG. 9, a total of 3100 included timesequence windows (920, 921) were determined within each of said signals(902, 903) [i.e. within each signal (902, 903) a total of 500 excludedtime sequence windows 922, 923 were determined]. For each of said 3100included, i.e. accepted, time sequence windows Time Sequence[1] to TimeSequence[3100] (920, 921), the time sequence (TS.x)-related parameters(924, 925) TS.MeanWavedT and TS.MeandT were determined, and the relatedtime sequence (rTS.x) parameters (926) rTS.MeanWavedT and rTS.MeandTwere computed for each individual of said 3100 time sequence windows(920, 921). Subsequently, the mean value of these parameters (926) weredetermined for all included time sequence windows (920, 921) of saidpressure recording Recording[62] (901). For both the related timesequence (rTS.x) parameters (926) rTS.MeanWavedP and rTS.MeandP, themean value was 0.86. Furthermore, eight test recordings were made(Recording[61], Recording[62], . . . Recording[68]) with similarsimultaneous intra-dural and epidural signals as in Recording[62]:Recording[62].Signal[1].ICP.Intra-dural;Recording[62].Signal[2].ICP.Epidural. Said population of recordings wascategorized according to signal type since only simultaneous intra-duraland epidural signals were included in said recordings (901). For thispopulation of recordings, the related time sequence (rTS.x) parameters(926) rTS.MeanWavedP and rTS.MeandP were determined according toequation (84).

Further aspects of determination of these related time sequence (rTS.x)parameters will now be explained. Some general comments are madeconcerning related time sequences (rTS.x) and factorized time sequences(fTS.x) with focus on notation. Through the invention it has been founduseful to include separate notation related to said related timesequences (rTS.x) and factorized time sequences (fTS.x). Simultaneouslyincluded time sequences from two or more simultaneous signals are termedPerfect Time Sequences (i.e. PerfectTS). Perfect time sequences(PerfectTS) only include identical included time sequences from two ormore signals wherein time reference is identical between all includedtime sequences. So-called Perfect Recording includes said Perfect TimeSequences, giving this notation: PerfectRecording[s].Signal[m].PerfectTS [u].

The term PopulationCount (PopCount) relates to a defined group ofPerfect Time Sequences (PerfectTS[u]), wherein said defined group isdetermined by attributes such as signal type, location and sensor type.

In general, a related time sequence (rTS) is defined by equation (82):rTS=ƒ(x)  (82)

According to this equation, rTS is a function (ƒ) of input x, wherein xis Perfect time sequences (PerfectTS) in general.

One example of input (x) is shown and expressed in equation (83):ƒ(x)=ƒ1(PerfectTS.MeanWavedP)+ƒ2(PerfectTS.MeanWavedP _(—)STD)+ƒn(PerfectTS.x)+ . . .  (83)

The example shown in equation (83) indicates that input (x) can be afunction of several Perfect time sequence (PerfectTS)-parameters.

In another example only the parameter PerfectTS.MeanWavedP is used asinput, among all possible PerfectTS.x parameters, as detailed inequation (84).

$\begin{matrix}{{{rTS}.{MeandP}} = {\left( {\sum\limits_{u = 1}^{PopCount}\frac{{ICP}.{Epidural}.{{PerfectTS}\lbrack u\rbrack}.{MeandP}}{{{ICP}.{Intra}} - {{dural}.{{PerfectTS}\lbrack u\rbrack}.{MeandP}}}} \right)/{PopCount}}} & (84)\end{matrix}$

The specific example given with reference to FIGS. 7 a and 7 b used thisspecific formula for determining the related time sequence parameterrTS.MeandP.

It should be noted as well that rTS [=ƒ(x)] can also be described with amuch more advanced mathematical method and formula-based relationshipsbetween the individual Perfect time sequence (PerfectTS.x) parameters.The specific examples shown here are intended to illustrate the concept,not to limit the scope of the invention.

Some general comments are also given with reference to the factorizedtime sequences. In general a factorized time sequence (fTS) can beexpressed according to equation (85):fTS[o]=ƒ(x)  (85)

A factorized time sequence (fTS[o]) is a function of input x, wherein xis an input signal, as specified in equation (86):fTS[o]=ƒ(Recording.Signal[m])  (86)

In this example, the input signal x is Recording.Signal[m], which is aspecified signal (Signal[m]) in a non-specified recording.

In another example, the input signal x is Signal.TS[o], which is aspecific perfect time sequence (PerfectTS[u]) in a non-specific signal,as shown in equation (87):fTS[o]=ƒ(Signal.PerfectTS[u])  (87)

These equations illustrate the concept that a factorized time sequence(fTS) is a result of a function that contains a method and/or formulabased on related time sequence (rTS) observations. Said method (formula)can be based on one or more of said PerfectTS.x-related parameters. Saidfactorized time sequences (fTS) thus can be considered as predicted timesequences as well.

Reference is again given to the population of test recordings(Recording[61] to Recording[68]) with similar simultaneous intra-duraland epidural signals as in Recording[62]:Recording[62].Signal[1].ICP.Intra-dural;Recording[62].Signal[2].ICP.Epidural. As already commented on, therelated time sequence (rTS.x) parameters (926) rTS.MeanWavedP andrTS.MeandP were determined according to equation (84) for thispopulation of recordings. The mean value for said population ofrecordings of related time sequence (rTS.x) parameters (926)rTS.MeanWavedP and rTS.MeandP were 0.83 and 0.87, respectively. Saidpopulation of recordings could also enable determination ofpopulation-based formulas for said related time sequence (rTS.x)-relatedparameters (926). It should be noted that the related time sequence(rTS.x) parameters rTS.MeanWavedP and rTS.MeandP only establishrelationships for the pressure scale of said pressure recordings. Inthis context is as well explained the process of determining factorizedtime sequence (fTS.x) parameters, derived from related time sequence(rTS.x) values (926) of continuous pressure-related signals ofindividual pressure recordings. According to this example, factorizedtime sequence (fTS.x) parameters could be derived from related timesequence (rTS.x) values (926) of continuous pressure-related signals ofa population of pressure recordings (Recording[61] to Recording[68]).For this population, rTS.MeanWavedP was equal to 0.83 and rTS.MeandPequal to 0.87. Since the epidural continuous signals are so-callednon-gold standard signals, it may be useful factorize the time sequenceparameters of these epidural signals. Factorization of such a specificsignal is performed as follows: For said signal (903) all the includedtime sequence windows (921) are determined according to the IdentifyingSteps shown in FIG. 9, and time sequence parameters (925) determined forsaid included time sequence windows (921). For example, a factorizedtime sequence (fTS.x) parameter fTS.MeandP is created by multiplying thetime sequence TS[x].MeandP parameter of each of said included timesequence windows with the value 0.87 (see equation 87). This is only oneexample of creating factorized time sequence (fTS.x)-related parameters,and this specific example is not intended to limit the scope of theinvention. The fTS.MeandP could as another example be calculated upon amultiple parameter relation such as rTS.x[1], rTS.x[2] . . . , whereinx[1] is the first related parameter and x[2] is the second relatedparameter. It is referred to equations 85 and 86.

Said creation of factorized time sequence (fTS.x) parameters relate toformula-based adjustment of time sequence (TS.x)-related parameters ofindividual time sequence windows of continuous pressure-related signals,said factorized time sequence (fTS.x) parameters can be selected fromthe group of:

-   -   factorized mean value of starting diastolic minimum pressure of        a time sequence window (fTS.MeanP_(min1)),    -   factorized standard deviation of mean value of starting        diastolic minimum pressure of a time sequence window        (fTS.MeanP_(min1) _(—) STD),    -   factorized mean value of systolic maximum pressure of a time        sequence window (fTS.MeanP_(max)),    -   factorized standard deviation of mean value of systolic maximum        pressure of a time sequence window (fTS.MeanP_(max) _(—) STD),    -   factorized mean amplitude of a time sequence window        (fTS.MeandP),    -   factorized standard deviation of mean amplitude of a time        sequence window (fTS.MeandP_STD),    -   factorized mean latency of a time sequence window (fTS.MeandT),    -   factorized standard deviation of mean latency of a time sequence        window (fTS.MeandT_STD),    -   factorized mean rise time coefficient of a time sequence window        (fTS.MeanRT),    -   factorized standard deviation of mean rise time coefficient of a        time sequence window (fTS.MeanRT_STD),    -   factorized mean wave duration of a time sequence window        (fTS.MeanWD),    -   factorized standard deviation of mean wave duration of a time        sequence window (fTS.MeanWD_STD),    -   factorized mean single wave pressure of a time sequence window        (fTS.Mean_(SW)P),    -   factorized standard deviation of mean single wave pressure of a        time sequence window (fTS.Mean_(SW)P_STD),    -   factorized mean value of diastolic minimum pressure difference        of a time sequence window (fTS.MeanDiff_P_(min)),    -   factorized standard deviation of mean value of diastolic minimum        pressure difference of a time sequence window        (fTS.MeanDiff_P_(min) _(—) STD),    -   factorized mean value of systolic maximum pressure difference of        a time sequence window (fTS.MeanDiff_P_(max)),    -   factorized standard deviation of mean value of systolic maximum        pressure difference of a time sequence window        (fTS.MeanDiff_P_(max) _(—) STD),    -   factorized mean amplitude difference of a time sequence window        (fTS.MeanDiff_dP),    -   factorized standard deviation of mean amplitude difference of a        time sequence window (fTS.MeanDiff_dP_STD),    -   factorized mean latency difference of a time sequence window        (fTS.MeanDiff_dT),    -   factorized standard deviation of mean latency difference of a        time sequence window (fTS.MeanDiff_dT_STD),    -   factorized mean rise time coefficient difference of a time        sequence window (fTS.MeanDiff_RT),    -   factorized standard deviation of mean rise time coefficient        difference of a time sequence window (fTS.MeanDiff_RT_STD),    -   factorized mean wave duration difference of a time sequence        window (fTS.MeanDiff_WD),    -   factorized standard deviation of mean wave duration difference        of a time sequence window (fTS.MeanDiff_WD_STD),    -   factorized standard deviation of mean single wave pressure        difference of a time sequence window        (fTS.MeanDiff_Mean_(SW)P_STD),    -   factorized amplitude of the mean wave of a time sequence window        (fTS.MeanWavedP),    -   factorized latency of the mean wave of a time sequence window        (fTS.MeanWavedT), and    -   factorized rise time coefficient of the mean wave of a time        sequence window (fTS.MeanWaveRT).

Said formula-based adjustment of time sequence (TS.x)-related parameterscan be related to multiplication of the pressure scale of saidindividual time sequence windows of said pressure-related signal with agiven constant factor value derived from the related time sequence(rTS.x) parameters. Said formula-based adjustment of time sequence(TS.x)-related parameters can also be related to adjustment of thepressure scale of said individual time sequence windows of saidpressure-related signal according to a formula relationship derived fromthe related time sequence (rTS.x) parameters.

Reference is now given to FIGS. 10 a, 10 b, 10 c, and 10 d in order tofurther give details about analyzing simultaneous continuouspressure-related signals derived from different locations inside and/oroutside of a body and/or body cavity, with determination of related timesequence (rTS.x) parameters, and using said related time sequence(rTS.x) parameters for creating factorized time sequence (fTS.x)parameters. FIG. 10 relates to simultaneous sampling of pressure-relatedsignals derived from within the brain parenchyma (Signal[1]) and fromthe outer ear channel (Signal[2]) after air tight sealing of said outerear channel.

In paragraphs [270] to [272] is given reference to a fourth feature ofthis invention. Said fourth feature of the invention relates to a devicefor use in sensing continuous pressure-related signals throughnon-invasive pressure measurements on a human body, comprising apressure sensor with a pressure sensing tube, said tube insertable intoa human or animal outer ear channel spaced from a tympanic membranethereof, and inflatable means surrounding an outside length of the tube,said inflatable means upon inflation thereof sealingly closing anannular gap between a region of said tube and a wall region of saidouter ear channel. Reference is now given to FIG. 10 a, showing indetail such a device for obtaining air pressure signals from within anairtight sealed outer ear channel. In FIG. 10 a is shown a schematicpresentation of an ear 1001, wherein ear skin surface forms the outerwall of the outer ear channel 1002 and the middle ear 1003, separated bythe tympanic membrane 1004. Continuous pressure-related signalsindicative of intracranial pressure are obtained by a sensor 1005measuring air pressure within the outer ear channel 1002. A pressuresensing tube 1006 enables the sensor 1005 to be in contact with theclosed air chamber within the outer ear channel 1002. This outer earchannel 1002 is closed by an inflatable means 1007 that is an inflatablethin walled balloon within the ear 1001 and the outer ear channel 1002,surrounding the pressure sensing tube 1006. Said inflatable balloon 1007gives air tight sealing of the outer ear channel 1002 by being filledwith fluid using a release duct 1008. Fluid could be e.g. air or water.Fluid filling of said inflatable balloon 1007 enables closing of theouter ear channel 1002. Thereby the air within the outer ear channel1002 is separated from the atmospheric pressure. Pressure fluctuationsare created by movement of the tympanic membrane 1004, since thetympanic membrane 1004 moves along with the pressure fluctuations withinthe middle ear 1003. The pressure gradient 1009 for each pressure waveis indicated with an arrow. Said inflatable means 1007 is deflatable byopening the release duct 1008, or by puncturing the inflatable means1007. The non-invasive ear channel related sensor 1005 is connected viaa signal transducer 1010 to a processing unit 1011, said processing unit1011 being capable of delivering continuous pressure-related signals. Itmust be understood that said pressure sensor 1005 could be locatedanywhere along said pressure sensing tube 1006, said sensor 1005location being within the ear channel 1002 or outside the ear channel1002.

The significance of this device is that said inflatable means 1007 isthin-walled and soft, thus making airtight sealing of an outer earchannel 1002 possible, independent on the diameter of the outer earchannel 1002. Thereby one size of said sensor may be used independent ofthe diameter of the outer ear channel.

The opportunity to measure air pressure within the outer ear channel1002 after airtight sealing of said channel 1002 is known in the priorart, based on the knowledge that is described in this paragraph. Theintracranial pressure waves are transmitted via fluid pathways from thesubarachnoid space to the perilymphatic duct (cochlear aqueduct). Thispathway is the primary conduit between the subarachnoid space and theperilymphatic spaces, and is connected to the inner ear where thepressure waves cause motion of the oval (and round) window and theossicles, leading to motion of the tympanic membrane 1004. If the outerear channel 1002 is sealed in an airtight fashion, these motions of thetympanic membrane 1004 cause air-pressure fluctuations 1009 that can berecorded using a special sensor 1005.

Reference is now given to FIGS. 10 b and 10 c, in order to illustrateprocessing of two simultaneous continuous pressure-related signals, inthis case derived from within the brain parenchyma (Signal[1] 1012) andfrom the outer ear channel 1002 (Signal[2] 1013), as described in detailwith reference to FIG. 10 a. This particular example is taken from atest recording Recording[241]. It is shown single pressure waves 1014 ofSignal[1] and single pressure waves of Signal[2] 1015. Both signals1012, 1013 have identical time reference, indicated by identical timescale 1016 for both signals 1012, 1013. It is evident that the pressurescales of Signal[1] 1017 and Signal[2] 1018 are different with differentscaling, related to the fact that said signals 1012, 1013 are measuredwith different sensors, having different characteristics. The timesequence window 1019 of Signal[1] and the time sequence window 1020 ofSignal[2] are identical according to time and is referred to asRecording[241].Signal[1].Time Sequence[12] andRecording[241].Signal[2].Time Sequence[12]. Within said time sequencewindows 1019, 1020 (Time Sequence[12] or TS[12]), single pressure waves1014, 1015 related to cardiac beat-induced pressure waves within saidtwo simultaneous signals 1012, 1013 are identified. With reference toFIG. 10 b, it is indicated that included pair combinations of valleysand peaks in said signal 1012 are identified, corresponding to includedpair combinations of a systolic maximum pressure value (SW.P_(max)) 1021a diastolic minimum pressure value (SW.P_(min1)) 1022, characterizingsingle pressure waves 1014 created by the cardiac beat-induced pressurewaves. Within this signal 1012 the ending diastolic minimum pressure(SW.P_(min2)) 1023 of a first single pressure wave was the same asstarting diastolic minimum pressure (SW.P_(min1)) 1022 of the subsequentsecond single pressure wave. The systolic maximum pressures (SW.P_(max))1021 are indicated as filled squares, and the diastolic minimumpressures (P_(min1)/P_(min2)) 1022, 1023 are shown as open circles. Withreference to FIG. 10 c, it is as well indicated for Signal[2] 1013 theincluded pair combinations of a systolic maximum pressure value(SW.P_(max)) 1024 a diastolic minimum pressure value (SW.P_(min1)) 1025.Also for this fragment of the signal 1013, the ending diastolic minimumpressure (SW.P_(min2)) 1026 of a first single pressure wave was the sameas starting diastolic minimum pressure (SW.P_(min1)) 1025 of thesubsequent second single pressure wave. For each of said time sequencewindows 1019, 1020 (Time Sequence[12]), were computed the time sequence(TS.x)-related parameters TS.MeandP and TS.MeanWavedP, as well as therelated time sequence (rTS.x) parameters rTS.MeandP and rTS.MeanWavedP,as shown in Table 8.

TABLE 8 Data related to time sequence windows shown in FIGs. 10b and10c. Time sequence (TS.x)-related parameters TS.MeanWavedP TS.MeandPSignal[1] 2.75 mmHg  2.82 mmHg Signal[2] 20.7 mmHg 21.46 mmHg Relatedtime sequence (rTS.x) values rTS.MeanWavedP rTS.MeandP 0.133 0.131(=2.75/20.7) (=2.82/21.46)

The recording incorporating Signal[1] 1012 and Signal[2] 1013 included atotal of 500 time sequence windows (Time Sequence[1] to TimeSequence[500]) within each of said signals 1012, 1013 (Signal[1] andSignal[2]). The method for processing said signal 1012, 1013 illustratedby the Identifying Steps in FIG. 9 were applied said 500 time sequencewindows, and only 40 time sequence windows [referred to asPerfectTimeSequences (PerfectTS)] were included since strict Single Wave& Delta Single Wave Criteria and Time Sequence & Delta Time SequenceCriteria were used. For these 40 included time sequence windows(PerfectTS) both the related time sequence (rTS.x) parametersrTS.MeanWavedP and rTS.MeandP were computed. The mean value ofrTS.MeandP of said 40 time sequence windows was 0.126. This value wassubsequently used for creating fTS.MeandP. This aspect of the method isfurther illustrated in FIG. 10 d. The consecutive numbers of said 40included time sequence windows (PerfectTS) are indicated on the x scale1027. Pressure values are indicated on the y scale or pressure scale1028. The trend plot of TS.MeandP 1029 of Signal[1] 1012 is indicatedfor said 40 included time sequence windows. In addition, the trend plotof fTS.MeandP 1030 of Signal[2] 1013 is indicated. Factorization ofSignal[2] was performed by multiplying the parameter TS.MeandP of eachindividual of said 40 time sequence windows with the factor 0.126. Whenconsidering the trend plots in FIG. 10 d, the mean value of all 40 timesequence windows of TS.MeandP 1029 of Signal[1] 1012 was 2.98±0.28 andthe comparable mean value of mean fTS.MeandP 1030 of Signal[2] 1013 was2.99±0.21. Thus, after factorization TS.MeandP of Signal[2] 1013 wasnearly identical to TS.MeandP of Signal[1] 1012. Also the comparablefluctuations of TSMeandP 1029 and fTS.MeandP 1030 are shown in FIG. 10d.

The data presented in FIGS. 10 b, 10 c and 10 d provide illustrationsthat computation of related (rTS.x) and factorized (fTS.x) time sequenceparameters can be very useful for non-invasive pressure measurements.This inventive feature provides a technical solution to this yetunsolved problem related to non-invasive pressure measurements.

Reference is now given to FIGS. 11 a and 11 b that illustrate the sameaspects as already described for FIGS. 10 a, 10 b and 10 c. Oneindividual recording contained two signals:Recording[250].Signal[1].ICP.Intradural;Recording[250].Signal[2].ICP.EarProbe. This means that both signals werecontinuous intracranial pressure (ICP)-related signals, wherein thesensor location was intra-dural (or within the brain parenchyma forSignal[1]) and non-invasive within the outer ear channel for Signal[2]within the same recording Recording[250]. The method of derivingcontinuous pressure-related signals indicative of intracranial pressureby measuring air pressure within the outer ear channel was described indetail for FIG. 10 a. For the case referred to in FIGS. 11 a and 11 b,both Signal[1] and Signal[2] contained 3000 time sequence windows each(Time Sequence[1] to Time Sequence[3000]). The method described indetail for FIG. 9 was applied to both signals, leaving a total of 750included and simultaneous time sequence windows within each of said twosignals, i.e. 750 perfect time sequence windows (PerfectTS) wereincluded. For both of said signals (Signal[1] and Signal[2]) the timesequence (TS.x) related parameter TS.MeandP were computed. Trend plotsof TS.MeandP for these signals are shown in FIG. 11 a, with numbers ofconsecutive included time sequence windows on the x scale 1101. Thepressure scale 1102 is indicated on the y scale. Trend plot TS.MeandP1103 of Signal[1] is indicated as well as trend plot TS.MeandP 1104 ofSignal[2]. It is obvious that the y scaling of said two signals(Signal[1] and Signal[2]) are very different related to the fact thatthese signals were derived from different types of sensors and that thecalibration of Signal[2] against atmospheric pressure was unknown. Foreach of these 750 simultaneous consecutive time sequence windows, therelated time sequence (rTS.x) parameter rTS.MeandP was computed for eachindividual of said time sequence windows. The mean value of said 750rTS.MeandP values was 0.137. Factorization of Signal[2] was performed bymultiplying the parameter TS.MeandP of each individual of said 750 timesequences (PerfectTS) with the value 0.137, creating fTS.MeandP. The xscale 1101 is identical in FIGS. 11 a and 11 b. Concerning the y scale1102, the scaling has been increased from in FIG. 11 a to FIG. 1 b. Thetrend plot of TS.MeandP 1103 of Signal[1] is indicated in FIG. 11 bwhich is the same as signal 1103 in FIG. 11 a. The trend plot of thefactorized time sequence (fTS.x) parameter fTS.MeandP 1105 of Signal[2](1104 in FIG. 11 a) is as well shown in FIG. 11 b. It should be notedthat after factorization, the trend plots of TS.MeandP of 1103 Signal[1]and fTS.MeandP 1105 of Signal[2] followed each other closely, as clearlynoted from FIG. 11 b. When considering the trend plots in FIG. 11 b, themean value of all 750 time sequence windows (PerfectTS) of TS.MeandP1103 of Signal[1] was 3.2±0.4 and the comparable mean value of meanfTS.MeandP 1105 of Signal[2] was 3.2±0.5. Thus, after factorizationTS.MeandP of Signal[2] was practically identical to TS.MeandP ofSignal[1]. Also the comparable fluctuations of TSMeandP 1103 andfTS.MeandP 1105 are shown in FIG. 11 b.

The example shown in FIGS. 11 a and 11 b demonstrates that intracranialpressure (ICP) can be measured by placing a sensor within the outer earchannel. The basis for pressure signal processing is disclosed inconnection with FIG. 9.

Both in FIGS. 10 and 11 two different simultaneous pressure-relatedsignals were compared, wherein one signal (Signal[1]) was derived fromwithin the brain parenchyma and the other signal (Signal[2]) was derivedfrom within the outer ear channel (see FIG. 10 a). In these examplesSignal[1] may be considered as “gold standard” and Signal[2] as“non-gold” standard. Since the absolute zero pressure level is unknownfor Signal[2], scaling of said pressure (y) scale is impossible. This isthe problem with all types of non-invasive pressure monitoring, asrelated to currently used, prior art technology. Since the absolutepressure level relative to atmospheric pressure is unknown, continuouspressure-related signals derived from allocation on the body (i.e.non-invasively) only reveal changes in pressures without informationwhether the pressures are abnormal or not. By means of the presentinvention this problem is solved. The procedure is described exemplifiedby the locations (i.e. outer ear channel versus brain parenchyma) andsensors described for FIGS. 10 and 11:

-   i) Simultaneous signals from the same recordings are compared as    described in detail for FIGS. 9, 10 and 11. Related time sequence    (rTS.x) parameters are determined based on time sequence    (TS.x)-related parameters only from included time sequence windows.    The Single Wave & Delta Single Wave Criteria and Time Sequence &    Delta Time Sequence Criteria used for identifying included time    sequence windows (see FIG. 9) are very strict in order to exclude    pressure waves related to artifacts or a combination of artifacts    and cardiac beat-induced pressure waves. Strict criteria are used to    ensure that the included single pressure waves of said included time    sequence windows are single pressure waves related to cardiac    beat-induced pressure waves. These included, i.e. accepted, time    sequence windows are referred to as perfect time sequences    (PerfectTS, or pTS).-   ii) Related time sequence (rTS.x) parameters are determined for a    population of recordings, and said relationships (rTS.x) may be    stored in a database. The recordings may be categorized, e.g.    related to signal type, locations and sensor types as well as    physiological categorizations. Said related time sequence (rTS.x)    parameters may be different types of relationships:    -   Constant relationships of rTS.x. Said related time sequence        (rTS.x) parameters can be constant relationships between        identical time sequence (TS.x)-related parameters of different        pressure signals with identical time reference. This approach        has been described in detail for rTS.MeandP (FIGS. 10 b, 10 c,        10 d, 11 a, and 11 b), though rTS.x may be derived from any        TS.x-related parameters.    -   Formula-based rTS.x relationships of related time sequences.        Said related time sequence (rTS.x) parameters can be        formula-based relationships between identical time sequence        (TS.x)-related parameters of different pressure signals with        identical time reference. Such formulas may be derived from a        combination of time sequence (TS.x)- and related time sequence        (rTS.x) parameters. The invention set no limitations concerning        which formula-based relationships that are possible. Based on a        population of recordings, determination of population-based        formulas for said related time sequence (rTS.x)-related        parameters is made possible.-   iii) Said determined related time sequence (rTS.x) parameters are    used for factorization of individual continuous pressure-related    signals. Said factorization is particularly useful for non-invasive    pressure-related signals. Said non-invasive signals are typically    obtained without a simultaneous invasive signal. Said related    relationships (rTS.x) stored in a database represent a historical    material of known relationships that are used to factorize new and    individual recordings. Some examples of continuous pressure-related    signals that can be factorized according to this invention are    mentioned: Continuous pressure-related signals indicative of    intracranial pressure (ICP) may be derived from an invasive sensor    measuring epidural pressure or from non-invasive sensors measuring    air pressure within outer ear channel (as described for FIG. 10 a),    or transcranial Dopppler signals, cranial impedance-related signals,    cranial fontanel applanation pressure signals, or ocular applanation    pressure signals. Continuous pressure-related signals indicative of    arterial blood pressure may be derived from non-invasive sensors    measuring arterial applanation pressure signals, pulse oxymetry    signals, or from other physiological signals, being transformable    into pressure-related signals indicative of intra-arterial pressure    signals

Reference is now given to a fifth feature of this invention.Characteristics of this inventive feature are particularly illustratedin FIGS. 12 and 13, and to FIG. 8.

Reference is now given to FIG. 12 a. According to said fifth feature ofthis invention, it is described a device 1201 for use in draining excessfluid from a human brain or spinal fluid cavity 1202, comprising a firstdrainage tube 1203 having an inlet 1204 thereof located in said brain orspinal fluid cavity, said first drainage tube 1203 connected to theinlet 1205 of a fluid flow controllable valve 1206, a valve-openingregulator 1207 with associated control unit 1208 being connected to aregulator 1209 and processing unit 1210, the control output from whichis a function of pressure-sensing signals derived from at least onepressure sensor 1211, a location of said sensor enabling continuouspressure-related signals to be derived from said human brain or spinalfluid cavity 1202, a pressure transducer 1212 transforming saidpressure-sensing signals into signals processed by said processing unit1210, a power supply 1213 connected thereto, information transferablemeans 1214, and a second drainage tube 1215 from an outlet 1216 of saidvalve opening having a distal outlet 1217 thereof, said distal outlet1217 opening into said another human body cavity 1218. Said device 1201including its components first drainage tube 1203, fluid flowcontrollable valve 1206, said valve-opening regulator 1207, said controlunit 1208, said regulator 1209, said processing unit 1210, said pressuretransducer 1212, said power supply 1213, said information transferablemeans 1214 and said second drainage tube 1215 being located below a skinsurface of said human body.

In a xy-chart shown in FIG. 12 b, it is indicated on the y-axisincreasing valve opening 1219 and on the x-axis increasing fluid flowrate 1220, and a line 1221 indicates the relationship between degrees ofvalve opening 1219 and fluid flow rate 1220. This is shown to illustratethe fluid flow rate 1220 through said fluid flow controllable valve 1206changes with increasing valve opening 1219 of said valve 1206. Aspecific type of relationship 1221 between valve opening 1219 and fluidflow rate 1220 represents no limitation of the scope of the invention,neither is a specific type of valve opening a limitation of the scope ofthe invention.

Said inlet 1204 of said first drainage tube 1203 can be located insidedura mater of a brain, within one or more of said brain fluid cavities.Typical locations can be within one of the cerebral ventricles withincerebrospinal fluid (CSF) compartments. Thereby the first drainage tube1203 is able to drain fluid from said brain fluid cavities to the inlet1205 of said fluid flow controllable valve 1206. Said 1204 inlet of afirst drainage tube 1203 can as well be located inside dura mater of aspinal cord within the cerebrospinal fluid (CSF) cavity at the spinallevel. Normally the cerebral and spinal cerebrospinal fluid (CSF)compartments are in direct communication, though blockade of fluidpathways may be present anywhere along the cranio-spinal fluid pathways.By placement of said first drainage tube 1203 within a spinalcerebrospinal fluid (CSF) compartment, said first drainage tube 1203 isable to drain fluid from said spinal fluid cavity 1202 to the inlet 1204of said fluid flow controllable valve 1206.

A location of said sensor 1211 provides for sensing pressure-relatedsignals from said human brain or spinal fluid cavity 1202. Said sensor1211 location can be on or coupled to said first drainage tube 1203providing for sensing pressure-related signals from said fluid withinsaid first drainage tube 1203. For example, a sensor 1211 can be locatedon the tip of a ventricular catheter, e.g. first drainage tube 1203,placed within said cerebral ventricles. The connection wire between saidsensor 1211 and pressure transducer 1212 that is incorporated withinsaid device 1201 can be incorporated within the first drainage tube1203. This would be the preferable approach though a placement of asensor 1211 elsewhere than together with said first drainage tube 1203is possible. Typically said sensor 1211 location is in a region of saidbrain cavity or in a region of said spinal cavity.

The sensor 1211 is connected via a signal transducer 1212 to saidprocessing unit 1210, said processing unit 1210 is capable of deliveringa first control signal to said regulator 1209. The output of saidprocessing unit 1210 is set to control said control unit 1208 associatedwith said valve-opening regulator 1207, said control unit 1208controlling fluid flow mass rate 1220 through said valve 1206 associatedwith said valve-opening regulator 1207 determining valve opening level1219. The processing unit 1210 delivers a first control signal to saidregulator 1209, and said regulator 1209 delivers a second control signalunto a control unit 1208, wherein the function of said second controlsignal is a function of said first control signal. The second controlsignal delivered unto said control unit 1208 is able to modify the modeby which said control unit 1208 controls adjustment of saidvalve-opening regulator 1207. The fluid flow rate 1220 through saidvalve 1206 is adjustable by said valve-opening regulator 1207 beingcontrolled by said first control signal from said processing unit 1210.

Said device 1201 comprising partly said first drainage tube 1203, saidfluid flow controllable valve 1206, said valve-opening regulator 1207,said control unit 1208, said regulator 1209, said processing unit 1210,said pressure transducer 1212, said power supply 1213, said informationtransferable means 1214 and partly said second drainage tube 1215 arelocatable below a skin surface of said human body in a thoracic orabdominal body area. The device 1201 may thus be implanted in thethoracic area as is conventionally done for a cardiac pacemaker

Another body cavity 1218 may be a thoracic body cavity such as a heartcavity, abdominal body cavity such as an intra-peritoneal cavity. Theopening 1217 of said second drainage tube 1215 within said another bodycavity 1218 enables drainage of fluid into an abdominal or thoracic bodycavity 1218. Thereby fluid may be drained from said brain or spinalfluid body cavity 1202 into a thoracic or abdominal body cavity 1218.

The device incorporates information transferable means 1214 thatprovides for information transferal through skin between saidinformation transferable means 1214 and an external processing unit1222. Typically said external processing unit 1222 is placed on the skinsurface above and in distance from the information transferable means1214. Thus intact skin is between said information transferable means1214 and an external processing unit 1222. Communication and transferalof information is performed wireless using technology known in the priorart. Said information can be from said power supply 1213 relating toresidual power. It will be useful to record the residual battery supplyto plan replacement of battery or recharging of battery if arechargeable battery is used. Information can be derived from saidprocessing unit 1210 relating to analysis output of processing ofcontinuous pressure related signals. Continuous pressure-related signalsare processed according to the method of this invention, and saidexternal processing unit 1222 may read pressure recordings of a variablelength. It can also be possible to deliver information from saidexternal processing unit 1222 to the processing unit 1210 incorporatedin said device 1201. This information can relate to adjustment ofinteraction between analysis output of said processing unit 1210 and thelevel of said first control signal delivered thereof.

In short, the function of said device 1201 incorporates the followingsteps:

(1) Continuous pressure sensing signals derived from said brain orspinal fluid cavity 1202 are transferred from said sensor 1211 to saidpressure transducer 1212, modifying said signals into signals that canbe further processed by the processing unit 1210.

(2) The inventive method for processing continuous pressure-relatedsignals described in detail for FIG. 8 is applied to a selectableduration of said pressure signal. An analysis output is created based oncomputation of time sequence parameters (see FIG. 8). The method forprocessing of continuous pressure-related signals with reference to saiddevice 1201 is further described in detail with reference to FIGS. 13and 14.

(3) Based on said analysis output of said processing unit 1210, a firstcontrol signal is created and delivered into said regulator 1209. Saidfirst control signal is a function of said analysis output. This step isfurther described in detail with reference to FIG. 14.

(4) Within said regulator 1209 said first control signal is transformedunto a second control signal that is delivered unto a control unit 1208that is modifying the mode by which a valve opening regulator 1207 isfunctioning. Said valve-opening regulator modifies the valve openinglevel 1219 that in turn determines the fluid flow rate 1220 of saidfluid flow controllable valve 1206.

(5) The whole process is tuned in so that said analysis output of saidprocessing unit 1210 determines and controls the fluid flow rate 1220 ofsaid valve 1206.

It should be understood that modifications of said components andmodifications of functional interactions between said components arewithin the scope of this invention. A crucial aspect of the invention isthat valve opening level 1219 and hence fluid flow rate 1220 can bedetermined from said analysis output of said processing unit 1210,wherein said analysis output is determined by the inventive method ofprocessing continuous pressure-related signals. A further crucial aspectof the invention is the possibility of being able to locate the device1201 in the thoracic area of the body, providing a greater freedom ofconstruction of the device, and easier maintenance, repair andreplacement of components.

As compared to current and prior art technology, the device has severalmajor advantages, such as e.g.: (a) Optimal and physiological drainageof fluid is made possible. Optimal drainage is obtained when fluiddrainage is accompanied with normal cerebral compliance. The inventivemethod when incorporated within said processing unit in the inventivedevice or the inventive system enables control of drainage to a rategiving optimal cerebral compliance. (b) Current problems associated withdrainage such as over-drainage or under-drainage are eliminated. Theinventive method when used in a processing unit of the inventive deviceor the inventive system reveals too high or too low pressure and enablesthe device or the system to provide an output to adjust valve fluid flowrate thereafter. (c) Optimal placement of device. Since valve fluid flowrate is independent of the position of the patient, the device may beplaced the most optimal location for the particular case. For example,the device may be placed below the skin in the thoracic level,comparable to a conventional pacemaker placement. (d) Physiologicaldrainage from either brain or spinal fluid cavities. Since the inventivedevice or system processes pressure signals independent of a zeropressure level and also enables continuous pressure signals from spinalcerebrospinal fluid cavity when processed in the device or system to behighly predictive of intracranial pressure, when using the device orsystem the distal end of the first drainage tube can be placed withinthe spinal cerebrospinal fluid (CSF) cavity, making it unnecessary topenetrate the brain with a catheter.

Reference is now given to the sixth feature of this invention. Variousaspects related to this feature of the invention are particularly shownin FIG. 8, and also in connection with FIGS. 13 and 14.

This sixth inventive feature relates to a method for processingcontinuous pressure-related signals, as related to the use of saiddevice 1201. Pressure sensing signals are derived from said sensor 1211,and modified within said pressure transducer 1212 into signals that arefurther within said processing unit 1210. The process method has alreadybeen described in detail with reference to FIG. 8, and partly withreference to FIG. 2. Said method for processing continuouspressure-related signals derived from locations inside a human body orbody cavity, comprises the steps of obtaining samples of said signals atspecific intervals, and converting thus sampled pressure signals intopressure-related digital data with a time reference. For selectable timesequence windows the method comprises the further steps of identifyingfrom said digital data signal single pressure waves related to cardiacbeat-induced pressure waves, and identifying from said digital datasignal pressure waves related to artifacts or a combination of artifactsand cardiac beat-induced pressure waves. The different steps of thisprocess are reviewed in the Identifying Steps of FIG. 8. The method forprocessing the pressure-related signals further incorporates computingtime sequence (TS.x)-related parameters of said single pressure wavesduring individual of said included time sequence windows (ComputingStep; FIG. 8), and establishing an analysis output of said time sequence(TS.x)-related parameters for a selectable number of said time sequencewindows. For included time sequence windows said analysis output relatesto computing time sequence (TS.x)-related parameters, said parametersselected from the group of:

-   -   mean value of starting diastolic minimum pressures of a time        sequence window (TS.MeanP_(min1)),    -   standard deviation of mean value of starting diastolic minimum        pressures of a time sequence window (TS.MeanP_(min1) _(—) STD),    -   mean value of systolic maximum pressures of a time sequence        window (TS.MeanP_(max)),    -   standard deviation of mean value of systolic maximum pressures        of a time sequence window (TS.MeanP_(max) _(—) STD),    -   mean amplitude of a time sequence window (TS.MeandP),    -   standard deviation of mean amplitude of a time sequence window        (TS.MeandP_STD),    -   mean latency of a time sequence window (TS.MeandT),    -   standard deviation of mean latency of a time sequence window        (TS.MeandT_STD),    -   mean rise time coefficient of a time sequence window        (TS.MeanRT),    -   standard deviation of mean rise time coefficient of a time        sequence window (TS.MeanRT_STD),    -   mean wave duration of a time sequence window (TS.MeanWD),    -   standard deviation of mean wave duration of a time sequence        window (TS.MeanWD_STD),    -   mean single wave pressure of a time sequence window        (TS.Mean_(SW)P),    -   standard deviation of mean single wave pressure of a time        sequence window (TS.Mean_(SW)P_STD),    -   mean of diastolic minimum pressure differences of a time        sequence window (TS.MeanDiff_P_(min)),    -   standard deviation of mean of diastolic minimum pressure        differences of a time sequence window (TS.MeanDiff_P_(min) _(—)        STD),    -   mean of systolic maximum pressure differences of a time sequence        window (TS.MeanDiff_P_(max)),    -   standard deviation of mean of systolic maximum pressure        differences of a time sequence window (TS.MeanDiff_P_(max) _(—)        STD),    -   mean amplitude difference of a time sequence window        (TS.MeanDiff_dP),    -   standard deviation of mean amplitude difference of a time        sequence window (TS.MeanDiff_dP_STD),    -   mean latency difference of a time sequence window        (TS.MeanDiff_dT),    -   standard deviation of mean latency difference of a time sequence        window (TS.MeanDiff_dT_STD),    -   mean rise time coefficient difference of a time sequence window        (TS.MeanDiff_RT),    -   standard deviation of mean rise time coefficient difference of a        time sequence window (TS.MeanDiff_RT_STD),    -   mean wave duration difference of a time sequence window        (TS.MeanDiff_WD),    -   standard deviation of mean wave duration difference of a time        sequence window (TS.MeanDiff_WD_STD),    -   mean single wave pressure difference of a time sequence window        (TS.MeanDiff_Mean_(SW)P),    -   standard deviation of mean single wave pressure difference of a        time sequence window (TS.MeanDiff_Mean_(SW)P_STD),    -   numbers of accepted single pressure waves of a time sequence        window (TS.SWCount),    -   mean wave amplitude of a time sequence computed according to the        first matrix (TS.MeanWavedP),    -   mean wave latency of a time sequence computed according to the        first matrix (TS.MeanWavedT),    -   mean wave rise time coefficient of a time sequence computed        according to the second matrix (TS.MeanWaveRT).

The methods for computing said parameters have already been described indetail with reference to FIGS. 2 and 8. Any of said time sequence(TS.x)-related parameters can be used to create said analysis output.Concerning regulation of said valve opening level 1219 and fluid flowrate 1220, in a preferred mode of the invention it has been found thatthe following parameters are most useful: Mean wave amplitude(TS.MeanWavedP), mean wave latency (TS.MeanWavedT), mean wave rise timecoefficient (TS.MeanWaveRT), and mean amplitude (TS.MeandP), meanlatency (TS.MeandT), and mean rise time coefficient (TS.MeanRT).However, other parameters as mentioned could also be used in addition oras replacements.

The various time sequence (TS.x)-related parameters have a differentrole in regulation of a valve, such as e.g:

(a) Quality control that the continuous pressure signal is of goodquality (i.e. that time sequence windows included for analysis containsingle pressure waves created by cardiac beat-induced pressure waves) ispredicted by TS-parameters such as: TS.MeandT, TS.MeandT_STD, TS.MeanWD,TS.MeanWD_STD, TS.MeanDiff_dT, TS.MeanDiff_dT_STD, TS.MeanDiff_WD,TS.MeanDiff_WD_STD, TS.SWCount, and TS.MeanWavedT. In particular, it isuseful to combine values of several of said parameters.

(b) Determination of reduced or increased cerebral compliance (i.e.whether intracranial pressure (ICP) is abnormally high or low) ispredicted by TS-parameters such as: TS.MeandP, TS.MeandP_STD, TS.MeanRT,TS.MeanRT_STD, TS.MeanDiff_dP, TS.MeanDiff_dP_STD, TS.MeanWavedP, andTS.MeanWaveRT. It can be very useful to combine the values of several ofthese parameters. This particular aspect is described in connection withFIGS. 13 a and 13 b, including the parameters TS.MeanWavedP andTS.MeanWavedT. In FIG. 13 a is as well illustrated that changes incerebral compliance can not be revealed by current and prior arttechnology (illustrated by the TS-parameter TS.MeanICP that is computedaccording to prior art technology).

(c) Prediction of absolute intracranial pressure (ICP) (i.e. absolutepressure relative to atmospheric pressure) is made by TS-parameters suchas: TS.MeanP_(min1), TS.MeanP_(min1) _(—) STD, TS.MeanP_(max),TS.MeanP_(max) _(—) STD, TS.MeandP, TS.MeandP_STD, TS.MeanRT,TS.MeanRT_STD, TS.Mean_(SW)P, TS.Mean_(SW)P_STD, TS.MeanDiff_P_(min),TS.MeanDiff_P_(min) _(—) STD, TS.MeanDiff_P_(max), TS.MeanDiff_P_(max)_(—) STD, TS.MeanDiff_dP, TS.MeanDiff_dP_STD, TS.MeanDiff_RT,TS.MeanDiff_RT_STD, TS.MeanDiff_Mean_(SW)P, TS.MeanDiff_Mean_(SW)P_STD,TS.MeanWavedP, and TS.MeanWaveRT. Values of these differentTS-parameters are combined to predict absolute intracranial pressure(ICP).

Based on said analysis output, a deliverable first control signal isestablished, said first control signal being determined according to oneor more selectable criteria for said analysis output. Subsequently saidfirst control signal is modified within said regulator 1209 into asecond control signal. Said second control signal is a function of saidfirst deliverable control signal, and is applicable as a deviceperformance-modifying signal. Within said device 1201, said deviceperformance-modifying signal can modify a valve opening regulator 1207,controlling a shunt-valve fluid flow rate 1220.

Said selectable criteria are determined on basis of analysis output of aselectable number of individual time sequence windows in a continuousseries of said time sequence windows. For example, said selectablecriteria relate to selectable thresholds for time sequence(TS.x)-related parameters, said parameters selected from the group of:mean wave amplitude (TS.MeanWavedP), mean wave latency (TS.MeanWavedT),mean wave rise time coefficient (TS.MeanWaveRT), mean amplitude(TS.MeandP), mean latency (TS.MeandT), and mean rise time coefficient(TS.MeanRT). Exact thresholds for said time sequence (TS.x)-relatedparameters are not given, not to limit the scope of the invention.However, in order to illustrate aspects with such selectable criteria,an example of criteria related to TS.MeanWavedP is shown in Table 9.

TABLE 9 Selectable criteria related to TS.MeanWavedP. Processing Unit:Processing Unit: Analysis Output Performance Output TS.MeanWavedP FirstControl Valve Opening Valve Fluid Flow Thresholds* Signal Level LevelRate Level 1.0-3.0 mmHg 0 0 0 3.0-4.0 mmHg 1 1 1 4.0-5.0 mmHg 2 2 25.0-7.0 mmHg 3 3 3  >7.0 mmHg 4 4 4 *TS.MeanWave dP Threshold relates toa mean value of mean wave amplitude (TS.MeanWavedP) for a selectablenumber of said individual time sequence windows.

In this specific example, said deliverable first control signal fromsaid processing unit 1210 unto said regulator 1209 has five levels; atFirst Control Signal Level zero, the corresponding Valve Opening Levelis zero, the corresponding Valve Fluid Flow Rate Level is zero,corresponding to no flow of fluid through said valve 1206. On the otherhand, at First Control Signal Level four, the corresponding ValveOpening Level is four, the corresponding Valve Fluid Flow Rate Level isfour, corresponding to maximum flow of fluid through said valve 1206.Thresholds of said time sequence (TS.x)-related parameters may berelated to individual of said parameters or to combinations of saidparameters. The first and second control signal levels may be possibleto adjust according to said criteria. The specific levels referred to inTable 8 should not limit the scope of the invention.

Reference is now given to FIGS. 13 a, 13 b, and 13 c in order to furtherillustrate how the results of the inventive method of processingpressure-related signals can be used to modify a shunt valve openinglevel.

First the background of the example is explained. A woman with so-callednormal-pressure hydrocephalus (NPH) received a Codman-HAKIM™ adjustableshunt. This shunt consists of the following components, as related tothe device shown in FIG. 12 a: A ventricular catheter (corresponding tothe first drainage tube 1203) was placed within the cerebral ventricles(corresponding to the first brain fluid cavity 1201), and connected tothe shunt valve (corresponding to the fluid flow adjustable valve 1206).This shunt valve (1206) then was connected to a distal catheter(corresponding to the second drainage tube 1215) that was introduced tothe intra-peritoneal cavity (corresponding to another body cavity 1218).The shunt contained an adjustable fluid flow controllable valve. Thesystem was placed below the skin of this woman. By applying a regulatoronto the intact skin surface, the fluid flow of said valve could beadjusted. The shunt function is well known from the prior art and notdescribed in more detail here.

With reference to FIGS. 13 a, 13 b and 13 c is shown three periods ofpressure measurement, with the time scale 1301 on the x-axis. For thefirst pressure measurement the adjustable valve was adjusted to 16 cmH₂O, for the second pressure recording the valve resistance was 12 cmH₂O and for the third time period the valve resistance was 8 cm H₂O. Theterms 16 cm H₂O, 12 cm H₂O and 8 cm H₂O refer to different degrees ofvalve opening, with increased valve opening the lower resistanceapplied. The mechanism by which an adjustable vale is draining fluid isnot a part of this invention and is not described in more detail. Inthis case a Codman ICP sensor was placed within the brain parenchyma,thus enabling the simultaneous ICP recording. The sensor is tunneledoutside the skin, connected to an external pressure transducer (CodmanICP Express), and analyzed according to the method described in thisinvention. The y-axis of FIG. 13 a indicates the pressure scale 1302 ofmean intracranial pressure (ICP) computed according to current andexisting technology. The trend plot 1303 of mean ICP is also indicated.It must be remembered that mean ICP is computed according to currentlyused, prior art technology as the sum of pressure levels divided bynumber of samples, independent whether the signal contains pressurewaves related to cardiac beats or artifacts. It is clearly shown thatmean ICP did not change during adjustment of shunt valve opening. On theother hand, highly observable changes were found for the trend plots ofthe TS.x-parameters TS.MeanWavedP 1305 and TS.MeanWavedT 1307 that arecomputed according to the inventive method. Both these latter parametersare highly indicative of intracranial compliance.

In FIG. 13 b is indicated the pressure scale 1304 of TS.MeanWavedP withthe trend plot of TS.MeanWavedP 1305. In FIG. 13 c is shown on theY-axis the TS.MeanWavedT scale 1306 as well as the trend plot ofTS.MeanWavedT 1307. Valve opening level 1308 was 16 cm H₂O before thefirst measurement period 1309, valve opening level 1310 was 12 cm H₂Obefore the second measurement period 1311, and valve opening level 1312was 8 cm H₂O before the third measurement period 1313. It should benoted that though valve resistance was reduced, mean ICP (computedaccording to current and prior art technology and not a part of thisinvention) did not change much between the measurement periods one 1309to three 1313 (mean ICP 10.5±3.0 mmHg for measurement period one, meanof trend plot of mean ICP 7.6±3.3 mmHg for measurement period two, andmean ICP 8.9±3.6 mmHg for measurement period three). Therefore, mean ICPwas within normal ranges during all the three measurement periods, andcurrently used, prior art methods for evaluating pressure changes wereof no help. Mean wave amplitude (TS.MeanWavedP) 1305, on the other hand,changed during adjustment of the shunt (mean wave amplitude 4.7±1.6 mmHgfor measurement period one, mean wave amplitude 3.6±1.0 mmHg formeasurement period two, and mean wave amplitude 3.2±0.5 mmHg formeasurement period three). In test recordings, it has already beenestablished that the presence of mean wave amplitudes (TS.MeanWavedP)above 4.5 mmHg is abnormal, and highly predictable for a good responseto extra-cranial shunt treatment. Therefore, during shunt adjustmentmean wave amplitudes (TS.MeanWavedP) were normalized. It should also benoted that mean wave latency (TS.MeanWavedT) values were as wellnormalized during shunt valve adjustment (mean wave latency 0.26±0.04seconds for measurement period one, mean wave latency 0.24±0.05 secondsfor measurement period two, and mean wave latency 0.21±0.04 seconds formeasurement period three). The down-regulation of shunt valve resistancewas accompanied by a clinical improvement of the patient.

With reference to Table 9, this specific example illustrates thefollowing: After measuring mean wave amplitudes (TS.MeanWavedP) above4.5 mmHg for a selectable time (e.g. said first measurement period 1309;FIG. 3 b), said First Control Signal Level would be adjusted to level 2,corresponding to a Valve Opening Level 2 and a Valve Fluid Flow RateLevel 2. After a new measurement period of a selectable time (e.g. saidsecond measurement period 1311; FIG. 13 b) measuring mean waveamplitudes (TS.MeanWavedP) between 3 and 3.5 mmHg, said First ControlSignal Level would remain at 1, corresponding to a Valve Opening Levelremaining at 1 and a Valve Fluid Flow Rate Level remaining at 1. Theduration of pressure measurement is selectable. By means of thisapproach the inventive device 1201 would ensure best possible drainageof excess fluid within said brain or spinal fluid cavities.

Reference is now given to the seventh feature of this invention.Characteristics related to this feature of the invention areparticularly shown in FIG. 14, and also in connection with FIG. 8.

According to said seventh feature of this invention is described asystem for processing continuous pressure-related signals 1401 derivablefrom one or more sensor(s) 1402 having location(s) inside or outside abody or body cavity of a human being. Said system comprises means for onbasis of said signals 1401 receivable from said sensor(s) 1402 viapressure transducer means 1403 to control drainage fluid flow rate froma first body cavity to a second body cavity in one said human. Aprocessing device 1404 in said system has means for processing saidsignals 1401, said processing means including sampling means 1405 forsampling said signals 1401 at specific intervals, converter means 1406for converting the sampled signals 1401 into pressure related digitaldata 1407 with a time reference, identifying means 1408 for duringselectable time sequence windows identifying from said digital data 1407single pressure waves related to cardiac beat-induced pressure waves,and related to artifacts or a combination of cardiac beat-induced wavesand artifacts, means for computing 1409 and analyzing 1410 said digitaldata 1407 during said selectable time sequence windows, output means1411 for outputting to device terminal means 1412 one or more pressureparameter signals related to a selectable number of said time sequencewindows, such as preferably mean wave amplitude (TS.MeanWavedP), meanwave latency (TS.MeanWavedT), mean wave rise time coefficient(TS.MeanWaveRT), mean amplitude (TS.MeandP), mean latency (TS.MeandT),mean rise time coefficient (TS.MeanRT), and mean single wave pressure(TS.Mean_(SW)P). The system includes a valve device 1413 controlling thedrainage fluid flow rate and connectable to said body cavities. Aregulator unit means 1414 connectable to said terminal means 1412 forreceiving at least one of said parameter signals, said regulator unitmeans 1414 being capable of establishing a device performance modifyingsignal by means of one of said pressure parameter signals or acombination effect obtained from using at least two of said pressureparameter signals, wherein said performance modifying signal deliverablefrom said regulator unit 1414 is capable of controlling said drainagefluid flow rate through said valve device 1413 by input to a controlunit 1415 and therefrom to a valve-opening regulator 1416. Saidperformance modifying signal is a function of pressure parametersignals, said function being related to selectable criteria for saidpressure parameter signals.

The system incorporates a power supply 1417 locatable below skin surfaceof said human being and incorporates transfer means 1418 enablinginformation transfer through skin of said human being, said informationbeing deliverable to an external processing unit 1419, said informationincluding one or more of power supply 1417 power status, valve device1413 performance data, parameter signals available or used.

It should be understood that modifications of said components andmodifications of functional interactions between said components arewithin the scope of this invention.

The location of said sensor 1402 provides for sensing pressure-relatedsignals 1401 from either a brain body cavity or a spinal body cavity.Drainage of fluid flow rate is from a first body cavity to a second bodycavity in one said human, and said first body cavity can be a brain orspinal body cavity, said second body cavity relates to a thoracic bodycavity or an abdominal body cavity.

Location of a valve device between first and second body cavities isappreciated from studying the schematic drawing figure of FIG. 12 a. Thesystem is locatable below a skin surface of said human body in athoracic or abdominal body area.

Said processing device 1404 in said system provides for processing saidsignals 1401 according to the method described for FIGS. 2 and 8. Withreference to previous descriptions related to FIGS. 2 and 8, a shortoverview of the inventive system for processing of continuous pressuresignals 1401 is now given with reference to FIG. 14.

The identifying means 1408 the method provides for identification of allseparate peaks and valleys 1420 in said sampled signal 1401. Each ofsaid peaks is a sample with a pressure value and a time stamp orlocation, and each of said valleys is a sample with a pressure value anda time stamp or location. The result of applying General MethodsCriteria 1421 is either included peak/valley pair combinations 1422 orexcluded peak/valley pair combinations 1423.

After applying the Single Wave & Delta Single Wave Criteria 1424 to saidincluded peak/valley pairs 1422, the output is either included singlepressure waves 1425 or excluded pressure waves 1426. Said criteria 1424relate to thresholds and ranges of single pressure wave (SW.x)-relatedparameters and delta single pressure wave (ΔSW.x)-related parametersduring time sequence windows.

After applying the Single Wave & Delta Single Wave Criteria 1424,included pair combinations of peak/valley pairs 1422 in said signal 1401correspond to included single pressure waves 1425. Pair combinations ofdiastolic minimum pressure (SW.P_(min1)) and systolic maximum pressure(SW.P_(max)) characterize single pressure waves created by cardiacbeat-induced pressure waves. Said criteria 1424 exclude for furtheranalysis pressure waves (i.e. minimum-maximum pressure(SW.P_(min1)/SW.P_(max)) pairs) during said time sequence windows withsaid single pressure wave (SW.x)- and delta single pressure wave(ΔSW.x)-related parameters outside selected criteria for thresholds andranges of said parameters. Said criteria 1424 include for furtheranalysis single pressure waves 1425 having single pressure wave (SW.x)-and delta single pressure wave (ΔSW.x)-related parameters withinselected criteria for thresholds and ranges of said single pressure wave(SW.x)-related parameters. Pair combinations of diastolic minimumpressure (SW.P_(min1)) and systolic maximum pressure (SW.P_(max))correspond to diastolic minimum pressures and systolic maximum pressuresof individual of pressure waves created by each of said cardiac beats.

In order to further evaluate the included single pressure waves 1425,Time Sequence & Delta Time Sequence Criteria 1427 are applied to each ofsaid time sequence windows in a continuous series of said time sequencewindows. Each time sequence window is a selected time frame of saidsignal 1401. Said criteria 1427 for thresholds and ranges of said timesequence (TS.x) and delta time sequence (ΔTS.x)-related parametersdetermine included, i.e. accepted, time sequence windows 1428 andexcluded, i.e. rejected, time sequence windows 1429. Said criteria 1427exclude for further analysis time sequence windows 1429 with timesequence (TS.x)- and delta time sequence (ΔTS.x)-related parametersoutside selected criteria for thresholds and ranges of said parameters.Said criteria 1427 include for further analysis time sequence windows1428 having time sequence (TS.x) and delta time sequence (ΔTS.x)-relatedparameters within selected criteria for thresholds and ranges of saidtime sequence (TS.x) and delta time sequence (ΔTS.x)-related parameters.

Using said computing means 1409, time sequence (TS.x)-related parameters1430 are computed for each individual of said included time sequencewindows 1428. Such procedure is applied to each of said included, i.eaccepted, time sequence windows 1428 in a continuous series of said timesequence windows. Using said computing means 1409, time sequence(TS.x)-related parameters 1430 from included time sequences windows 1428are computed, said parameters can be selected from the group of:

-   -   mean value of starting diastolic minimum pressures of a time        sequence window (TS.MeanP_(min1)),    -   standard deviation of mean value of starting diastolic minimum        pressures of a time sequence window (TS.MeanP_(min1) _(—) STD),    -   mean value of systolic maximum pressures of a time sequence        window (TS.MeanP_(max)),    -   standard deviation of mean value of systolic maximum pressures        of a time sequence window (TS.MeanP_(max) _(—) STD),    -   mean amplitude of a time sequence window (TS.MeandP),    -   standard deviation of mean amplitude of a time sequence window        (TS.MeandP_STD),    -   mean latency of a time sequence window (TS.MeandT),    -   standard deviation of mean latency of a time sequence window        (TS.MeandT_STD),    -   mean rise time coefficient of a time sequence window        (TS.MeanRT),    -   standard deviation of mean rise time coefficient of a time        sequence window (TS.MeanRT_STD),    -   mean wave duration of a time sequence window (TS.MeanWD),    -   standard deviation of mean wave duration of a time sequence        window (TS.MeanWD_STD),    -   mean single wave pressure of a time sequence window        (TS.Mean_(SW)P),    -   standard deviation of mean single wave pressure of a time        sequence window (TS.Mean_(SW)P_STD),    -   mean of diastolic minimum pressure differences of a time        sequence window (TS.MeanDiff_P_(min)),    -   standard deviation of mean of diastolic minimum pressure        differences of a time sequence window (TS.MeanDiff_P_(min) _(—)        STD),    -   mean of systolic maximum pressure differences of a time sequence        window (TS.MeanDiff_P_(max)),    -   standard deviation of mean of systolic maximum pressure        differences of a time sequence window (TS.MeanDiff_P_(max) _(—)        STD),    -   mean amplitude difference of a time sequence window        (TS.MeanDiff_dP),    -   standard deviation of mean amplitude difference of a time        sequence window (TS.MeanDiff_dP_STD),    -   mean latency difference of a time sequence window        (TS.MeanDiff_dT),    -   standard deviation of mean latency difference of a time sequence        window (TS.MeanDiff_dT_STD),    -   mean rise time coefficient difference of a time sequence window        (TS.MeanDiff_RT),    -   standard deviation of mean rise time coefficient difference of a        time sequence window (TS.MeanDiff_RT_STD),    -   mean wave duration difference of a time sequence window        (TS.MeanDiff_WD),    -   standard deviation of mean wave duration difference of a time        sequence window (TS.MeanDiff_WD_STD),    -   mean single wave pressure difference of a time sequence window        (TS.MeanDiff_Mean_(SW)P),    -   standard deviation of mean single wave pressure difference of a        time sequence window (TS.MeanDiff_Mean_(SW)P_STD),    -   numbers of accepted single pressure waves of a time sequence        window (TS.SWCount),    -   mean wave amplitude of a time sequence window computed according        to the first matrix (TS.MeanWavedP),    -   mean wave latency of a time sequence window computed according        to the first matrix (TS.MeanWavedT),    -   mean wave rise time coefficient of a time sequence window        computed according to the second matrix (TS.MeanWaveRT).

The methods for computing said parameters 1430 have already beendescribed in detail with reference to FIGS. 2 and 8. Any of said timesequence (TS.x)-related parameters 1430 can be used to create saidanalysis output 1431 from an analysis means 1410. Concerning regulationof said valve 1413, in a preferred mode of the invention it has beenfound that the following parameters are most useful: Mean wave amplitude(TS.MeanWavedP), mean wave latency (TS.MeanWavedT), mean wave rise timecoefficient (TS.MeanWaveRT), mean amplitude (TS.MeandP), mean latency(TS.MeandT), mean rise time coefficient (TS.MeanRT), and mean singlewave pressure (TS.Mean_(SW)P).

There are several major advantages with computing one or more of saidparameters:

-   a) Compliance within a brain or spinal body cavity can be assessed    by parameters e.g. such as TS.MeanWavedP, TS.MeanWavedT,    TS.MeanWaveRT, TS.MeandP, TS.MeandT, and (TS.MeanRT). This is not    possible by currently used, prior art technology. The inventive    system enables to control drainage of excess fluid in a way that    ensures optimal cerebral compliance.-   b) The quality of the pressure signals can be assessed by    parameters, e.g. such as TS.MeanP_(min1) _(—) STD, TS.MeanP_(max)    _(—) STD, TS.MeandP_STD, TS.MeandT_STD, TS.MeanRT_STD,    TS.MeanWD_STD, TS.MeanDiff_P_(min) _(—) STD, TS.MeanDiff_P_(max)    _(—) STD, TS.MeanDiff_dP_STD, TS.MeanDiff_dT_STD,    TS.MeanDiff_RT_STD, and TS.MeanDiff_WD_STD. It is very important to    quality control the signals since bad signal quality increases the    risk of not identifying cardiac beat-induced single pressure waves.    Such quality control is not possible with current, prior art    technology.-   c) The output of said identifying means 1408 is included time    sequence windows 1428 wherein said time sequence windows are    included the best possible way. This means that these time sequence    windows 1428 contain single pressure waves related to cardiac    beat-induced pressure waves, not to artifacts or a combination of    artifacts and cardiac beat-induced pressure waves. Thereby the risk    of computing false or misleading time sequence (TS.x)-related    parameters is made minimal.-   d) Combinations of said time sequence parameters 1430 provide more    detailed information about compliance and signal quality control    than by using only one of such parameters 1430.

By use of output means 1411 of said processing device 1404, output isestablished as parameter signals 1432, based on analysis output of saidanalyzing means 1410.

Concerning said identifying means 1408, the signal processing performedhas been described for FIGS. 2, 3 a, 3 b, 3 c, 4 a, 4 b, 5 a, 5 b, 6,and 7 a. Details about General Methods Criteria 1421, Single Wave &Delta Wave Criteria 1424 and Time Sequence & Delta Time SequenceCriteria 1427 are already commented on in detail with reference to FIG.2, and therefore these aspects are not commented on further in thiscontext.

Said output means 1411 enables establishment of one or more pressureparameter signals 1432 related to a selectable number of said timesequence windows for outputting to said device terminal means 1412. Aperformance-modifying signal established from the regulator unit means1414 is a function of one or more of said pressure parameter signals1432. Said function is related to selectable criteria for said pressureparameter signals 1432.

Said selectable criteria are determined on basis of analysis output 1431of a selectable number of included time sequence windows 1428. Exactthresholds for said time sequence (TS.x)-related parameters 1430 are notgiven, as they may be varied and should not therefore be considered tolimit the scope of the invention. However, in order to illustrateaspects with such selectable criteria, a non-limitative example ofcriteria related to TS.MeanWavedP is shown in Table 10. Table 10 isdirectly comparable to Table 9.

TABLE 10 Selectable criteria related to TS.MeanWavedP. Processing device(analysis means) Processing device Regulator unit means TS.MeanWavedPThreshold (output means) Performance- Criteria* Parameter signalmodifying signal 1.0-3.0 mmHg 0 0 3.0-4.0 mmHg 1 1 4.0-5.0 mmHg 2 25.0-7.0 mmHg 3 3  >7.0 mmHg 4 4 *TS.MeanWavedP Threshold relates to amean value of mean wave amplitude (TS.MeanWavedP) for a selectablenumber of said individual time sequence windows.

Since the parameter 1430 TS.MeanWavedP is highly predictable of cerebralcompliance, criteria related to this parameter are very useful forcontrolling said regulator unit means 1414 and in turn said valve device1413 for fluid drainage purposes. A major advantage is further obtainedby combining criteria related to two or more of said parameters 1430,since such combinations increase the accuracy by which cerebralcompliance in assessed and controlled. In addition, the accuracy of theprocess of including correct single pressure waves and time sequenceswindows containing cardiac beat-induced single pressure waves isincreased.

Reference is now given to the eighth feature of this invention. Thisfeature is further illustrated in FIG. 15, and also in FIG. 8.

Reference is now given to FIGS. 15 a, 15 b, 15 c, and 15 d. FIG. 15 ashows an inventive device and modifications of the device are shown inFIGS. 15 b, 15 c, and 15 d.

First, with reference to FIG. 15 a, is shown an inventive device 1501for use in sensing continuous pressure-related signals derivable fromlocations inside or outside a human or animal body or body cavity. Thedevice 1501 comprises a pressure sensor 1502 with a pressure sensingelement, a pressure transducer 1503 capable of transforming saidpressure-related signals into digital pressure-related signals, aprocessing unit 1504 with input means for receiving saidpressure-related digital signals, said processing unit 1504 providing atoutput means 1505 thereof one or more of the following time sequenceparameters during selectable time sequence windows of saidpressure-related signals: Mean wave amplitude (TS.MeanWavedP), mean wavelatency (TS.MeanWavedT), mean wave rise time coefficient(TS.MeanWaveRT), mean amplitude (TS.MeandP), mean latency (TS.MeandT),mean rise time coefficient (TS.MeanRT), and mean single wave pressure(TS.Mean_(SW)P). Furthermore, a display unit 1506 connected to saidoutput means 1505 for selectively displaying said one or moreparameters, and means for supplying power 1507 to power consuming partsof the device.

The device can be modified so that the means for supplying power 1507can be connected to external means for power supply 1507′, e.g. via acable to another external power source.

As shown in FIG. 15 a, said pressure sensor 1502, said pressuretransducer 1503, said processing unit 1504, said output means 1505, saiddisplay unit 1506 and said means for supplying power 1507 together canconstitute a single physical unit forming a display device 1501.

In FIG. 15 b is shown another variant of the device shown in FIG. 15 a.According to the device 1508 shown in FIG. 15 b, a pressure sensor 1509is connectable to the device 1508 at an input 1510 thereof linked to apressure transducer 1511. More specifically said device 1508 constitutesa single physical unit, comprising said pressure transducer 1511, aprocessing unit 1512, an output means 1513, a display unit 1514 andmeans 1515 for supplying power. Said pressure sensor 1509 is configuredto be located in tissue or body cavity or enclosed fluid flow part ofsaid human or animal body. Said single physical unit of said device 1508is locatable below or on a skin surface of said human or animal body orspaced externally from the skin surface.

The device can be modified so that the means for supplying power 1515can be connected to external means for power supply 1515′, e.g. via acable to another external power source.

In FIG. 15 c is described another modification of said device 1501already described in FIG. 15 a. The device 1501 can include parameterdata storage means and parameter selection control means.

Reference is now given to FIG. 15 c, illustrating a device 1516comprising a pressure sensor 1517, a pressure transducer 1518, aprocessing unit 1519, an output means 1520, a display unit 1521,parameter selection control means 1522, parameter storage means 1523,means 1524 for supplying power, these elements within the device 1516together constitute a single physical unit.

The device can be modified so that the means 1524 for supplying powercan be connected to external means for power supply 1524′, e.g. via acable to another external power source.

The combined sensor and display device 1516 constituting a singlephysical unit 1516 can as well, as an option, include informationtransfer means 1525. Information can be transferred from saidinformation transfer means 1525 to an external unit 1526. Saidtransferal of information to an external unit 1526 relates to visualdisplay of said information. Said external unit 1526 can be amulti-parameter vital signs monitor. When said device 1516 alsoincorporates said information transfer means 1525, said informationtransfer means 1525 is included in said single physical unit 1516.

Reference is now given to FIG. 15 d, which is a modification of thedevice shown in FIG. 15 c. In FIG. 15 d is shown a device 1527 wherein apressure sensor 1528 is connectable to said single physical unit 1527 atan input 1529 thereof linked to a pressure transducer 1530. The device1527 shown in FIG. 15 d comprises said pressure transducer 1530, aprocessing unit 1531, an output means 1532, a parameter selectioncontrol means 1533, a parameter storage means 1534, a display unit 1535,said means 1536 for supplying power, said elements within the unit 1527together constituting said single physical unit 1527. Said pressuresensor 1528 is configured to be suitably located in tissue or bodycavity or enclosed fluid flow part of said human or animal body, andwherein said single physical unit of said device 1527 is locatable belowor on a skin surface of said human or animal body or spaced externallyfrom the skin surface.

The device can be modified so that the means 1536 for supplying powercan be connected to external means for power supply 1536′, e.g. via acable to another external power source.

The display device 1527 constituting a single physical unit 1527 can aswell include information transfer means 1537. Information can betransferred from said information transfer means 1537 to an externalunit 1538. Said transferal of information to an external unit 1538relates to visual display of said information. Said external unit 1538can be a multi-parameter vital signs monitor. When said device 1527 alsoincorporates said information transfer means 1537, said informationtransfer means 1538 is also included in said single physical unit 1527.

With reference to FIGS. 15 a, 15 b, 15 c, and 15 d, said pressuresensors 1502, 1509, 1517, 1528 can be configured either to be locatedinside or outside a human or animal body or body cavity. Said sensor1502, 1509, 1517, 1528 can either be configured for measuring pressureswithin a fluid or within a solid tissue. Said sensors 1509, 1528 can belocated at a physical distance from said respective pressure transducer1511, 1530 and processing unit 1512, 1531.

With reference to FIGS. 15 a and 15 b said means 1507, 1515 forsupplying power could be a battery, e.g a rechargeable battery.

With reference to FIGS. 15 c and 15 d, said means 1524, 1536 forsupplying power can receive power from a respective external unit 1526,1538. Alternatively, said means for supplying power 1526, 1536 can be abattery, e.g. a rechargeable battery.

With reference to FIGS. 15 a, 15 b, 15 c, and 15 d said means 1507,1515, 1524, 1536 for supplying power can be connected to external meansfor power supply 1507′, 1515′, 1524′, 1536′. Such external means be acable contact to external power sources.

With reference to FIGS. 15 a, 15 b, 15 c, and 15 d, said device 1501,1508, 1516, and 1527 can be a self-contained system, or supplementary toan external multi-parameter display system as regards display ofpressure related parameters.

The major significance with the devices described in FIGS. 15 a, 15 b,15 c, and 15 d is that the inventive method of processing continuouspressure related signals can be directly displayed. Completely newinformation about pressures not revealed by currently used, though,prior art technology can be shown, enabling subsequent quick diagnosisto be made by an experienced physician, quit of intervention. Thevarious modifications of the device provides for pressure measurementsin many settings, from within the hospital department to the privatehome of a patient.

Reference is now given to the ninth feature of this invention. Thisfeature is particularly illustrated in FIG. 16, and also in FIG. 8.

Reference is now given to FIGS. 16 a, 16 b, 16 c, and 16 d. It is shownan inventive device in FIG. 16 a. Modifications of this device (FIG. 16a) are shown in FIGS. 16 b, 16 c, and 16 d.

With reference to FIG. 16 a, is shown a device 1601 for use in sensingcontinuous pressure-related signals derivable from locations inside oroutside a human or animal body or body cavity, comprising a pressuresensor 1602 with a pressure sensing element, a pressure transducer 1603capable of transforming said pressure-related signals into digitalpressure-related signals, a processing unit 1604 with input means forreceiving said pressure-related digital signals, said processing unit1604 providing at output means 1605 thereof one or more of the followingtime sequence parameters during selectable time sequence windows of saidpressure-related signals: Mean wave amplitude (TS.MeanWavedP), mean wavelatency (TS.MeanWavedT), mean wave rise time coefficient(TS.MeanWaveRT), mean amplitude (TS.MeandP), mean latency (TS.MeandT),mean rise time coefficient (TS.MeanRT), and mean single wave pressure(TS.Mean_(SW)P). Information transfer means 1606 is connected to saidoutput means 1605, and there is provided means 1607 for supplying powerto power consuming parts within the device. Said information transfermeans 1606 enables transferal of information to an external unit 1608 ofat least said one or more parameters.

Transferal of information to an external unit 1608 can relate to visualdisplay of said information, and wherein said external unit 1608 can bea multi-parameter vital signs monitor.

It is shown in FIG. 16 a that said device 1601 comprising said pressuresensor 1602, said pressure transducer 1603, said processing unit 1604,said output means 1605, said information transfer means 1606 and saidmeans for supplying power 1607 together constitute a single physicalunit forming a sensor device 1601.

Reference is now given to FIG. 16 b, to illustrate a device being amodification of the device shown in FIG. 16 a. In FIG. 16 b is shown asensor device 1609, wherein the pressure sensor 1610 is connectable tothe device 1609 at an input 1611 linked to said pressure transducer1612. Said sensor device 1609 comprises elements in the form of apressure transducer 1612, a processing unit 1613, an output means 1614,information transfer means 1615 and means 1616 for supplying power, saidelements together constituting a single physical unit. Said pressuresensor 1610 can be located in tissue or body cavity or enclosed fluidflow part of said human or animal body. Said single physical unitforming said device 1609 is locatable below or on a skin surface of saidhuman or animal body or spaced externally from said skin surface.

Reference is now given to FIG. 16 c, to show another modification of thedevice 1601 shown in FIG. 16 a. Said device 1601 is modified to furtherinclude a display unit 1623 connected to output means 1622 of aprocessing unit 1621 or linking said output means with informationtransfer means 1625.

In FIG. 16 c is shown a combined sensor-display device 1618, wherein theelements included therein are a pressure sensor 1619, a pressuretransducer 1620, a processing unit 1621, output means 1622, a displayunit 1623, means 1624 for supplying power and information transfer means1625 together constitute a single physical unit forming a sensor-displaydevice 1618.

The device can be modified so that the means 1624 for supplying powercan be connected to external means for power supply 1624′, e.g. via acable to another external power source.

In FIG. 16 d is shown a device 1627 representing a modification of thatshown in FIG. 16 c. In FIG. 16 d a pressure sensor 1628 is connectableto the device 1627 at an input 1629 thereof linked to a pressuretransducer 1630. Said pressure transducer 1630 together with aprocessing unit 1631, output means 1632, display unit 1633, said means1634 for supplying power and said information transfer means 1635constitute a single physical unit 1627. Said pressure sensor 1628 can beconfigured to be suitable for location in tissue or body cavity orenclosed fluid flow part of said human or animal body. Said singlephysical unit of said device 1627 is locatable below or on a skinsurface of said human or animal body or spaced externally from the skinsurface.

The device can be modified so that the means 1634 for supplying powercan be connected to external means for power supply 1634′, e.g. via acable to another external power source.

The sensor-display devices 1618, 1627 shown in FIGS. 16 c and 16 c, canfurther include parameter data storage means and parameter selectioncontrol means. The resulting devices have already been described inconnection with FIGS. 15 c and 15 d, therefore further details are notgiven here. The devices 1516, 1527 described in FIGS. 15 c and 15 d arecombined sensor-display devices.

With reference to FIGS. 16 a, 16 b, 16 c, and 16 d, said pressure sensor1602, 1610, 1619, 1628 can be configured either to be located inside oroutside a human or animal body or body cavity. Further, said sensors1602, 1610, 1619, 1628 can either be configured for measuring pressureswithin a fluid or within a solid tissue. Suitably, the sensors 1610,1628 can be located at physical distance from the respective pressuretransducer 1612, 1630 and processing unit 1613, 1631.

With reference to FIGS. 16 a and 16 b said means 1607, 1616 forsupplying power can be a battery, e.g. a rechargeable battery.

With reference to FIGS. 16 c and 16 d, said means 1624, 1634 forsupplying power can receive power from said external unit 1626, 1636.Alternatively, said means 1624, 1634 can be a battery, e.g. arechargeable battery.

With reference to FIGS. 16 a, 16 b, 16 c, and 16 d, said devices 1601,1609, 1618, 1627 can each be used as an independent, self-containedsensor system. The devices 1618, 1627 can also provide parameterdisplay, which will be supplementary to an externally locatedmulti-parameter display system.

By means of the inventive devices described in FIGS. 16 a, 16 b, 16 c,and 16 d the inventive method of processing continuous pressure relatedsignals can be incorporated directly within such sensor device, e.g.through use firmware or plug-in program related memory. Thereby thedevice can provide completely new information about pressures, ascompared to current and prior art technology useful for diagnostic ormonitoring purposed when viewed by a physician. For example, output fromthe device itself may be used to assess cerebral compliance without needfor further or additional equipment. For example, simple equipment canbe developed for diagnosing so-called normal pressure hydrocephalus(NPH). The sensor can measure pressure within the spinal cerebrospinalfluid (CSF) cavity, and the continuous pressure signals can be processedaccording to the inventive method, the output of the analysis can bedisplayed to the physician, thus allowing quick diagnosis based on thepersonal expertise of the physician. Thereby pressure related parametersand combination thereof have been made available through the invention,and can be subsequently viewed and evaluated by a physician for his/herdetermination of diagnosis or for mere status monitoring, even atlocations outside hospital grounds, at an outpatient clinic or in fieldoperations.

Reference is now given to the tenth feature of this invention. Thisfeature is further illustrated in FIG. 17, and also in FIG. 8.

Reference is now given to FIG. 17. In FIG. 17 is shown a system forprocessing continuous pressure-related signals 1706 derivable from oneor more sensor(s) 1702 configured to be suitable for location(s) insideor outside a body or body cavity of a human being. Said system comprisesmeans for on basis of said signals 1706 receivable from said sensor(s)1702 via pressure transducer means 1703 to display 1704 output of saidprocessing, either on a display unit 1704 or an external unit 1718. Aprocessing unit 1701 in said system comprises means for processing saidsignals 1706. The processing unit includes: (a) Sampling means 1705 forsampling said signals 1706 receivable from said pressure transducermeans 1703 at specific intervals; (b) Converter means 1707 forconverting the sampled signals 1706 received from said sampling meansinto pressure related digital data 1708 with a time reference; (c)Identifying means 1709 for during selectable time sequence windowsidentifying from said digital data 1708 output from said converter means1707 single pressure waves related to cardiac beat-induced pressurewaves, related to artifacts, or a combination of cardiac beat-inducedwaves and artifacts; (d) Computing means 1710 for computing timesequence parameters 1711 from included or selected time sequence windowsoutput from said identifying means; (e) Analyzing means 1712 foranalyzing said time sequence parameters 1711 in the form of digital datarelated to said selectable time sequence windows; (f) Output means 1714for outputting to device terminal means 1715 one or more pressureparameters related to a selectable number of said time sequence windows:Mean wave amplitude (TS.MeanWavedP), mean wave latency (TS.MeanWavedT),mean wave rise time coefficient (TS.MeanWaveRT), mean amplitude(TS.MeandP), mean latency (TS.MeandT), mean rise time coefficient(TS.MeanRT), and mean single wave pressure (TS.Mean_(SW)P). Said systemalso includes means for supplying power 1716 to power consuming partswithin the system.

Said system shown in FIG. 17 includes a display unit 1704 linked to saidoutput means 1714 via terminal means 1715.

The system of FIG. 17 can be incorporated in many types of physicalunits forming sensor devices, display devices, or a combination ofsensor and display devices. Some examples are given in FIGS. 15 a, 15 b,15 c, 15 d, 16 a, 16 b, 16 c and 16 d, in which the processor unit withits output means corresponds to the processor unit 1701 in FIG. 17 withits included output means 1714.

A system comprising said pressure sensor 1702, said pressure transducer1703, said processing unit 1701, said display unit 1704 and said meansfor supplying power 1716 can together constitute a single physical unitforming a display device. Such a physical unit forming a device isfurther described with reference to FIG. 15 a.

A system comprising said pressure transducer 1703, said processing unit1701, said display unit 1704 and said means for supplying power 1716together can also constitute a single physical unit. In this situationsaid pressure sensor 1702 is connectable to said single physical unit atan input linked to said pressure transducer 1703. Said pressure sensor1702 is configured to be suitably located in tissue or body cavity orenclosed fluid flow part of said human or animal body. Said singlephysical unit of said device is locatable below or on a skin surface ofsaid human or animal body or spaced externally from the skin surface.Such a physical unit forming a device is further described in FIG. 15 b.

Output means 1714 of said system shown in FIG. 17 can further beoperative with information transfer means 1717, enabling display of saidanalysis output 1713 on an external unit 1718 connectable to said outputmeans 1714 via the information transfer means 1717. Furthermore, outputmeans 1714 of said system shown in FIG. 17 can further include or beco-operate with parameter selection control means 1719 and parameterdata storage means 1720.

Transferal of information to an external unit 1718 can relate to visualdisplay of said information, and for such purpose said external unit1718 is suitably a multi-parameter vital signs monitor.

A system comprising said pressure sensor 1702, said pressure transducer1703, said processing unit 1701, said display unit 1704, said means forsupplying power 1716, said information transfer means 1717, saidparameter selection control means 1718, and said parameter data storagemeans 1720 together can constitute a single physical unit forming adisplay and sensor device. Such a physical unit forming a device isfurther described in FIG. 15 c.

Another system comprising said pressure transducer 1703, said processingunit 1701, said display unit 1704, said means for supplying power 1716,said information transfer means 1717, said parameter selection controlmeans 1719, and said parameter data storage means 1720 together canconstitute another single physical unit. In this situation, the pressuresensor 1702 is connectable to said single physical unit at an inputthereof linked to said pressure transducer 1703. Said pressure sensor1702 is configured to be suitably located in tissue or body cavity orenclosed fluid flow part of said human or animal body. Said singlephysical unit of said device is locatable below or on a skin surface ofsaid human or animal body or spaced externally from the skin surface.Such a physical unit forming a device is further described in FIG. 15 d.

A system comprising said pressure sensor 1702, said pressure transducer1703, said processing unit 1701, said information transfer means 1717and said means for supplying power 1716 together can constitute a singlephysical unit forming a sensor device. This single physical unit of saiddevice is locatable below a skin surface of or non-invasively relativeto said human or animal body. Such a single physical unit forming asensor device is further described in FIG. 16 a.

A system comprising said pressure transducer 1703, said processing unit1701, said information transfer means 1717 and said means for supplyingpower 1716 together can constitute a single physical unit. The pressuresensor 1702 can be connectable to said single physical unit at an inputlinked to said pressure transducer 1703. The pressure sensor 1702 can beconfigured to be suitably located in tissue or body cavity or enclosedfluid flow part of said human or animal body, and the single physicalunit of said device can be locatable below or on a skin surface of saidhuman or animal body or spaced externally from the skin surface. Such asingle physical unit forming a sensor device is further described inFIG. 16 b.

Output means 1714 of said system shown in FIG. 17 can further beoperative with a display unit 1704 connected to said output means 1714of said processing unit 1701 or linking said output means 1714 with saidinformation transfer means 1717 enabling connection to an optionalexternal unit 1718. In this situation a system comprising said pressuresensor 1702, said pressure transducer 1703, said processing unit 1701,said display unit 1704, said means for supplying power 1716 and saidinformation transfer means 1717 together can constitute a singlephysical unit forming a sensor device. Such a single physical unitforming a sensor device is further described in FIG. 16 c.

A system comprising said pressure transducer 1703, said processing unit1701, said display unit 1704, said means for supplying power 1716 andsaid information transfer means 1717 together can constitute a singlephysical unit. In this situation the pressure sensor 1702 is connectableto said single physical unit at an input thereof linked to said pressuretransducer 1703. The pressure sensor 1702 can be located in tissue orbody cavity or enclosed fluid flow part of said human or animal body,and the single physical unit of such device can be locatable below or ona skin surface of said human or animal body or spaced externally fromthe skin surface. Such a single physical unit forming a sensor device isfurther described in FIG. 16 d.

With reference to the system described in FIG. 17, said pressure sensor1702 can be configured to be located either inside or outside a human oranimal body or body cavity. Furthermore, said pressure sensor 1702 caneither be configured for measuring pressures within a fluid orconfigured for measuring pressures within a solid tissue. Said sensor1702 can be located at a physical distance from said pressure transducer1703 and processing unit 1701 when the sensor 1702 is connectable to thesingle physical unit which inter alia incorporates the pressuretransducer 1703.

Said means for supplying power 1716 referred to in FIG. 17 can receivepower from said external unit 1718 in the embodiment of FIGS. 15 c, 15d, 16 a-16 d. Alternatively or in combination said means for supplyingpower 1716 can be a battery, e.g. a rechargeable battery.

The system described in FIG. 17 can be a self-contained system. Thesystem, with reference to FIGS. 15 a-15 d, 16 c and 16 d provides foradd-on of display to a display device in an external multi-parameterdisplay system.

Said processing unit 1701 in said system provides for processing saidsignals 1706 according to the method described for FIGS. 2 and 8. Withreference to previous descriptions related to FIGS. 2 and 8, a shortoverview of the inventive system for processing of continuous pressuresignals 1701 is now given with reference to FIG. 17.

Using the identifying means 1709, the method provides for identificationof all separate peaks and valleys 1721 in said sampled signal 1706. Eachof said peaks is a sample with a pressure value and a time stamp orlocation, and each of said valleys is a sample with a pressure value anda time stamp or location. The result of applying General MethodsCriteria 1722 is either included peak/valley pair combinations 1723 orexcluded peak/valley pair combinations 1724.

After applying the Single Wave & Delta Single Wave Criteria 1725 to saidincluded peak/valley pairs 1723, the output is either included singlepressure waves 1726 or excluded pressure waves 1727. Said criteria 1725relate to thresholds and ranges of single pressure wave (SW.x)-relatedparameters and delta single pressure wave (ΔSW.x)-related parametersduring time sequence windows.

After applying the Single Wave & Delta Single Wave Criteria 1725,included pair combinations of peak/valley pairs 1723 in said signal 1706correspond to included single pressure waves 1726. Pair combinations ofdiastolic minimum pressure (SW.P_(min1)) and systolic maximum pressure(SW.P_(max)) characterize single pressure waves created by cardiacbeat-induced pressure waves. Said criteria 1725 exclude for furtheranalysis pressure waves (i.e. minimum-maximum pressure(SW.P_(min1)/SW.P_(max)) pairs) during said time sequence windows withsaid single pressure wave (SW.x)- and delta single pressure wave(ΔSW.x)-related parameters outside selected criteria for thresholds andranges of said parameters. Said criteria 1725 include for furtheranalysis single pressure waves 1726 having single pressure wave (SW.x)-and delta single pressure wave (ΔSW.x)-related parameters withinselected criteria for thresholds and ranges of said single pressure wave(SW.x)-related parameters. Pair combinations of diastolic minimumpressure (SW.P_(min1)) and systolic maximum pressure (SW.P_(max))correspond to diastolic minimum pressures and systolic maximum pressuresof individual of pressure waves created by each of said cardiac beats.

In order to further evaluate the included single pressure waves 1726,Time Sequence & Delta Time Sequence Criteria 1728 are applied to each ofsaid time sequence windows in a continuous series of said time sequencewindows. Each time sequence window is a selected time frame of saidsignal 1706. Said criteria 1728 for thresholds and ranges of said timesequence (TS.x) and delta time sequence (ΔTS.x)-related parametersdetermine included time sequence windows 1729 and excluded time sequencewindows 1730. Said criteria 1728 exclude for further analysis timesequence windows 1730 with time sequence (TS.x)- and delta time sequence(ΔTS.x)-related parameters outside selected criteria for thresholds andranges of said parameters. Said criteria 1728 include for furtheranalysis time sequence windows 1729 having time sequence (TS.x) anddelta time sequence (ΔTS.x)-related parameters within selected criteriafor thresholds and ranges of said time sequence (TS.x) and delta timesequence (ΔTS.x)-related parameters.

Using said computing means 1710, time sequence (TS.x)-related parameters1711 are computed for each individual of said included time sequencewindows 1729. Such procedure is applied to each of said included timesequence windows 1729 in a continuous series of said time sequencewindows. Using said computing means 1710, time sequence (TS.x)-relatedparameters 1711 from included time sequences windows 1729 are computed,said parameters can be selected from the group of:

-   -   mean value of starting diastolic minimum pressures of a time        sequence window (TS.MeanP_(min1)),    -   standard deviation of mean value of starting diastolic minimum        pressures of a time sequence window (TS.MeanP_(min1) _(—) STD),    -   mean value of systolic maximum pressures of a time sequence        window (TS.MeanP_(max)),    -   standard deviation of mean value of systolic maximum pressures        of a time sequence window (TS.MeanP_(max) _(—) STD),    -   mean amplitude of a time sequence window (TS.MeandP),    -   standard deviation of mean amplitude of a time sequence window        (TS.MeandP_STD),    -   mean latency of a time sequence window (TS.MeandT),    -   standard deviation of mean latency of a time sequence window        (TS.MeandT_STD),    -   mean rise time coefficient of a time sequence window        (TS.MeanRT),    -   standard deviation of mean rise time coefficient of a time        sequence window (TS.MeanRT_STD),    -   mean wave duration of a time sequence window (TS.MeanWD),    -   standard deviation of mean wave duration of a time sequence        window (TS.MeanWD_STD),    -   mean single wave pressure of a time sequence window        (TS.Mean_(SW)P),    -   standard deviation of mean single wave pressure of a time        sequence window (TS.Mean_(SW)P_STD),    -   mean of diastolic minimum pressure differences of a time        sequence window (TS.MeanDiff_P_(min)),    -   standard deviation of mean of diastolic minimum pressure        differences of a time sequence window (TS.MeanDiff_P_(min) _(—)        STD),    -   mean of systolic maximum pressure differences of a time sequence        window (TS.MeanDiff_P_(max)),    -   standard deviation of mean of systolic maximum pressure        differences of a time sequence window (TS.MeanDiff_P_(max) _(—)        STD),    -   mean amplitude difference of a time sequence window        (TS.MeanDiff_dP),    -   standard deviation of mean amplitude difference of a time        sequence window (TS.MeanDiff_dP_STD),    -   mean latency difference of a time sequence window        (TS.MeanDiff_dT),    -   standard deviation of mean latency difference of a time sequence        window (TS.MeanDiff_dT_STD),    -   mean rise time coefficient difference of a time sequence window        (TS.MeanDiff_RT),    -   standard deviation of mean rise time coefficient difference of a        time sequence window (TS.MeanDiff_RT_STD),    -   mean wave duration difference of a time sequence window        (TS.MeanDiff_WD),    -   standard deviation of mean wave duration difference of a time        sequence window (TS.MeanDiff_WD_STD),    -   mean single wave pressure difference of a time sequence window        (TS.MeanDiff_Mean_(SW)P),    -   standard deviation of mean single wave pressure difference of a        time sequence window (TS.MeanDiff_Mean_(SW)P_STD),    -   numbers of accepted single pressure waves of a time sequence        window (TS.SWCount),    -   mean wave amplitude of a time sequence window computed according        to the first matrix (TS.MeanWavedP),    -   mean wave latency of a time sequence window computed according        to the first matrix (TS.MeanWavedT),    -   mean wave rise time coefficient of a time sequence window        computed according to the second matrix (TS.MeanWaveRT).

The methods for computing said parameters 1711 have already beendescribed in detail with reference to FIGS. 2 and 8. Any of said timesequence (TS.x)-related parameters 1711 can be used to create saidanalysis output 1713 from an analyzing means 1712. Concerning the systemdescribed in FIG. 17, in a preferred mode of the invention it has beenfound that the following parameters are most useful: Mean wave amplitude(TS.MeanWavedP), mean wave latency (TS.MeanWavedT), mean wave rise timecoefficient (TS.MeanWaveRT), mean amplitude (TS.MeandP), mean latency(TS.MeandT), mean rise time coefficient (TS.MeanRT), and mean singlewave pressure (TS.Mean_(SW)P).

There are several major advantages with computing one or more of saidparameters:

Compliance within a brain or spinal body cavity can be assessed from theexpertise of a qualified physician by his/her studying of parameterssuch as e.g.: TS.MeanWavedP, TS.MeanWavedT, TS.MeanWaveRT, TS.MeandP,TS.MeandT, and (TS.MeanRT). This is not possible by currently use, priorart technology. The inventive system provides the physician with usefulaids by making available for new information not available withcurrently used, and prior art technology, thus enabling also morecomprehensive and relevant monitoring of pressure related signals andrender in the end a safer diagnosis of a patient to be made by aphysician. However, neither the inventive methods disclosed herein, northe devices/systems disclosed will in any way and by themselves be ableto provide any diagnosis of an illness or a physical defect, e.g. in thebrain. Thus, the pressure related parameters which are output to adisplay, possibly through an elevated selection of just some pressurerelated parameters, will provide the physician with aids which must befurther proceed by him/her to provide an expert diagnosis.

The quality of the pressure signals can be assessed by parameters, e.g.such asTS.MeanPmin1_STD, TS.MeanP_(max) _(—) STD, TS.MeandP_STD,TS.MeandT_STD, TS.MeanRT_STD, TS.MeanWD_STD, TS.MeanDiff_P_(min) _(—)STD, TS.MeanDiff_P_(max) _(—) STD, TS.MeanDiff_dP_STD,TS.MeanDiff_dT_STD, TS.MeanDiff_RT_STD, and TS.MeanDiff_WD_STD. It isvery important to quality control the signals since bad signal qualityincreases the risk of not identifying cardiac beat-induced singlepressure waves. Such quality control is not possible with current, priorart technology. Thus, the risk of obtaining misleading and falsepressure measurements is made minimal by using the provisions madeavailable through the present invention.

The output of said identifying means 1709 is included time sequencewindows 1729 wherein said time sequence windows are included the bestpossible way. This means that these time sequence windows 1729 containsingle pressure waves related to cardiac beat-induced pressure waves,not to artifacts or a combination of artifacts and cardiac beat-inducedpressure waves. Thereby the risk of computing false or misleading timesequence (TS.x)-related parameters is made minimal. This is veryimportant when e.g. a sensor is implanted on permanent basis into a bodyor body cavity. It is crucial to control that the signals measured aregood and that the measured pressures actually are predictive for thepressures within the body or body cavity.

Concerning said identifying means 1709 the processing performed therebyis identical to the process described for FIGS. 2, 3 a, 3 b, 3 c, 4 a, 4b, 5 a, 5 b, 6, and 7 a. Details about General Methods Criteria 1722,Single Wave & Delta Wave Criteria 1725 and Time Sequence & Delta TimeSequence Criteria 1728 are already commented on in detail with referenceto FIG. 2, and therefore these aspects are not commented on further inthis context.

It will be readily understood that a processing unit 1701 with outputmeans 1714 included therein and in the form of e.g. a personal desk-topor lap-top computer could be used for implementing the operationalfunctions 1721-1729, 1711 and 1713 through use of software loaded intothe computer in a conventional manner. However, with smaller physicaldimension of such processor and output means, the operational functionswould suitably be provided through use of firmware or use of plug-inprogram memory.

If the display unit 1506, 1514, 1525, 1537, 1625, 1635 or 1704 has adisplay in the form of a touch screen, the parameter selection controlmeans could partly be provided through a selection device operable fromusing the touch screen.

1. A method for processing two or more simultaneous continuouspressure-related signals derivable from a human or animal body from oneor more locations thereof electable from: inside the body, outside thebody, inside body cavity, outside body cavity, comprising the steps ofobtaining samples of said signals at specific intervals, and in aprocessing unit converting thus sampled pressure signals intopressure-related digital data with identical time reference, wherein forselectable and simultaneous time sequence windows the method comprisesusing the processing unit for the further steps of: a) identifying fromsaid digital data single pressure waves related to cardiac beat-inducedpressure waves within said two or more simultaneous signals constitutinga pressure recording, b) identifying from said digital data pressurewaves related to artifacts or a combination of artifacts and cardiacbeat-induced pressure waves within said two or more simultaneous signalsconstituting a pressure recording, c) computing time sequence(TS.x)-related parameters of said single pressure waves during saididentical time sequence windows within said two or more simultaneoussignals constituting a pressure recording, wherein the method in usingthe processing unit comprises additional steps of: d) determiningrelationships between time sequence (TS.x)-related parameters of saididentical time sequence windows within said two or more simultaneoussignals constituting a pressure recording, said relationships calculatedas related time sequence (rTS.x) parameters, and selected as one or morefrom the group of: d1) relationship of mean values of starting diastolicminimum pressure of two or more perfect time sequence windows from twoor more different pressure signals (rTS.Pmin1), d2) relationship ofstandard deviation of mean values of starting diastolic minimum pressureof two or more perfect time sequence windows from two or more differentpressure signals (rTS.MeanPmin1_STD), d3) relationship of mean values ofsystolic maximum pressure of two or more perfect time sequence windowsfrom two or more different pressure signals (rTS.MeanPmax), d4)relationship of standard deviation of mean values of systolic maximumpressure of two or more perfect time sequence windows from two or moredifferent pressure signals (rTS.MeanPmax_STD), d5) relationship of meanamplitude values of two or more perfect time sequence windows from twoor more different pressure signals (rTS.MeandP), d6) relationship ofstandard deviation of mean amplitude of two or more perfect timesequence windows from two or more different pressure signals(rTS.MeandP_STD), d7) relationship of mean latency of two or moreperfect time sequence windows from two or more different pressuresignals (rTS.MeandT), d8) relationship of standard deviation of meanlatency of two or more perfect time sequence windows from two or moredifferent pressure signals (rTS.MeandT_STD), d9) relationship of meanrise time coefficient of two or more perfect time sequence windows fromtwo or more different pressure signals (rTS.MeanRT), d10) relationshipof standard deviation of mean rise time coefficient of two or moreperfect time sequence windows from two or more different pressuresignals (rTS.MeanRT_STD), d11) relationship of mean wave duration of twoor more perfect time sequence windows from two or more differentpressure signals (rTS.MeanWD), d12) relationship of standard deviationof mean wave duration of two or more perfect time sequence windows fromtwo or more different pressure signals (rTS.MeanWD_STD), d13)relationship of mean single wave pressure of two or more perfect timesequence windows from two or more different pressure signals(rTS.MeanSWP), d14) relationship of standard deviation of mean singlewave pressure of two or more perfect time sequence windows from two ormore different pressure signals (rTS.MeanSWP_STD), d15) relationship ofmean diastolic minimum pressure difference of two or more perfect timesequence windows from two or more different pressure signals(rTS.MeanDiff_Pmin), d16) relationship of standard deviation of meandiastolic minimum pressure difference of two or more perfect timesequence windows from two or more different pressure signals(rTS.MeanDiff_Pmin_STD), d17) relationship of mean systolic maximumpressure difference of two or more perfect time sequence windows fromtwo or more different pressure signals (rTS.MeanDiff_Pmax), d18)relationship of standard deviation of mean systolic maximum pressuredifference of two or more perfect time sequence windows from two or moredifferent pressure signals (rTS.MeanDiff_Pmax_STD), d19) relationship ofmean amplitude difference of two or more perfect time sequence windowsfrom two or more different pressure signals (rTS.MeanDiff_dP), d20)relationship of standard deviation of mean amplitude difference of twoor more perfect time sequence windows from two or more differentpressure signals (rTS.MeanDiff_dP_STD), d21) relationship of meanlatency difference of two or more perfect time sequence windows from twoor more different pressure signals (rTS.MeanDiff_dT), d22) relationshipof standard deviation of mean latency difference of two or more perfecttime sequence windows from two or more different pressure signals(rTS.MeanDiff_dT_STD), d23) relationship of mean rise time coefficientdifference of two or more perfect time sequence windows from two or moredifferent pressure signals (rTS.MeanDiff_RT), d24) relationship ofstandard deviation of mean rise time coefficient difference of two ormore perfect time sequence windows from two or more different pressuresignals (rTS.MeanDiff_RT_STD), d25) relationship of mean wave durationdifference of two or more perfect time sequence windows from two or moredifferent pressure signals (rTS.MeanDiff_WD), d26) relationship ofstandard deviation of mean wave duration difference of two or moreperfect time sequence windows from two or more different pressuresignals (rTS.MeanDiff_WD_STD), d27) relationship of mean single wavepressure difference of two or more perfect time sequence windows fromtwo or more different pressure signals (rTS.MeanDiff_MeanSWP), d28)relationship of standard deviation of mean single wave pressuredifference of two or more perfect time sequence windows from two or moredifferent pressure signals (rTS.MeanDiff_MeanSWP_STD), d29) relationshipof single wave count of two or more perfect time sequence windows fromtwo or more different pressure signals (rTS.SWCount), d30) relationshipof mean wave amplitude of two or more perfect time sequence windows fromtwo or more different pressure signals (rTS.MeanWavedP), d31)relationship of mean wave latency of two or more perfect time sequencewindows from two or more different pressure signals (rTS.MeanWavedT),and d32) relationship of mean wave rise time coefficient of two or moreperfect time sequence windows from two or more different pressuresignals (rTS.MeanWaveRT), and e) determining said related time sequence(rTS.x) parameters for a selectable number of recordings, said relatedtime sequence (rTS.x) parameters used for formula-based adjustments oftime sequence (TS.x)-related parameters of individual pressure-relatedsignals.
 2. The method according to claim 1, each of said selectable andsimultaneous time sequence windows is a selected time frame of saidpressure-related digital data with a time reference, wherein saidselected time frame lies in the range 5-15 seconds.
 3. The methodaccording to claim 1, wherein each of said simultaneous selectable timesequence windows is related to a number of time-related sequentialpressure samples, each sample referenced by a sample number and elapsedtime determined by sample location number and sample frequency.
 4. Themethod according to claim 1, wherein said sampled signals are obtainedfrom at least two simultaneous continuous intracranial intra-dural andextra-cranial pressure signals indicative of intracranial pressuresignals.
 5. The method according to claim 4, wherein said extra-cranialpressure signals are transcranial Doppler signals, being transformableinto pressure-related signals indicative of intracranial pressuresignals.
 6. The method according to claim 4, wherein said extra-cranialpressure signals are cranial impedance-related signals, beingtransformable into pressure-related signals indicative of intracranialpressure signals.
 7. The method according to claim 4, wherein saidextra-cranial pressure signals are ocular applanation pressure signals.8. The method according to claim 1, wherein said computing step c) forincluded time sequence windows further includes determining said timesequence (TS.x)-related parameters of simultaneous signals constitutinga pressure recording, said parameters selected from the group of: c1)mean value of starting diastolic minimum pressures of a time sequencewindow (TS.MeanP_(min1)), c2) standard deviation of mean value ofstarting diastolic minimum pressures of a time sequence window(TS.MeanP_(min1) _(—) STD), c3) mean value of systolic maximum pressuresof a time sequence window (TS.MeanP_(max)), c4) standard deviation ofmean value of systolic maximum pressures of a time sequence window(TS.MeanP_(max) _(—) STD), c5) mean amplitude of a time sequence window(TS.MeandP), c6) standard deviation of mean amplitude of a time sequencewindow (TS.MeandP_STD), c7) mean latency of a time sequence window(TS.MeandT), c8) standard deviation of mean latency of a time sequencewindow (TS.MeandT_STD), c9) mean rise time coefficient of a timesequence window (TS.MeanRT), c10) standard deviation of mean rise timecoefficient of a time sequence window (TS.MeanRT_STD), c11) mean waveduration of a time sequence window (TS.MeanWD), c12) standard deviationof mean wave duration of a time sequence window (TS.MeanWD_STD), c13)mean single wave pressure of a time sequence window (TS.Mean_(SW)P),c14) standard deviation of mean single wave pressure of a time sequencewindow (TS.Mean_(SW)P_STD), c15) mean of diastolic minimum pressuredifferences of a time sequence window (TS.MeanDiff_P_(min)), c16)standard deviation of mean of diastolic minimum pressure differences ofa time sequence window (TS.MeanDiff_P_(min) _(—) STD), c17) mean ofsystolic maximum pressure differences of a time sequence window(TS.MeanDiff_P_(max)), c18) standard deviation of mean of systolicmaximum pressure differences of a time sequence window(TS.MeanDiff_P_(max) _(—) STD), c19) mean amplitude difference of a timesequence window (TS.MeanDiff_dP), c20) standard deviation of meanamplitude difference of a time sequence window (TS.MeanDiff_dP_STD),c21) mean latency difference of a time sequence window (TS.MeanDiff_dT),c22) standard deviation of mean latency difference of a time sequencewindow (TS.MeanDiff_dT_STD), c23) mean rise time coefficient differenceof a time sequence window (TS.MeanDiff_RT), c24) standard deviation ofmean rise time coefficient difference of a time sequence window(TS.MeanDiff_RT_STD), c25) mean wave duration difference of a timesequence window (TS.MeanDiff_WD), c26) standard deviation of mean waveduration difference of a time sequence window (TS.MeanDiff_WD_STD), c27)mean single wave pressure difference of a time sequence window(TS.MeanDiff_Mean_(SW)P), c28) standard deviation of mean single wavepressure difference of a time sequence window(TS.MeanDiff_Mean_(SW)P_STD), c29) number of accepted single pressurewaves of a time sequence window (TS.SWCount), c30) mean wave amplitudeof a time sequence computed according to a first matrix (TS.MeanWavedP),c31) mean wave latency of a time sequence computed according to a firstmatrix (TS.MeanWavedT), c32) mean wave rise time coefficient of a timesequence computed according to a second matrix (TS.MeanWaveRT).
 9. Themethod according to claim 1, wherein said related time sequence (rTS.x)parameters are constant relationships between identical time sequence(TS.x)-related parameters of different pressure signals with identicaltime reference.
 10. The method according to claim 1, wherein saidrelated time sequence (rTS.x) parameters are formula-based relationshipsbetween identical time sequence (TS.x)-related parameters of differentpressure signals with identical time reference.
 11. The method accordingto claim 1, wherein said related time sequence (rTS.x) parameters arecomputed for each individual of said time sequence windows in acontinuous series of time sequence windows, and wherein the mean valueof such parameters is determined for all individual time sequencewindows within a signal of a pressure recording.
 12. The methodaccording to claim 1, wherein in step e) comprising said related timesequence (rTS.x) parameters determined for a selectable number ofrecordings, said selectable number of recordings each including onecontinuous pressure-related signal or at least two simultaneouscontinuous pressure-related signals.
 13. The method according to claim12, wherein said selectable number of recordings is categorizedaccording to signal type.
 14. The method according to claim 12, whereinsaid selectable number of recordings enables determination of selectablerecording based formulas for said related time sequence (rTS.x)-relatedparameters.
 15. The method according to claim 1, wherein saidformula-based adjustments of time sequence (TS.x)-related parameters arederived from related time sequence (rTS.x) values of continuouspressure-related signals of individual pressure recordings.
 16. Themethod according to claim 1, wherein said formula-based adjustments oftime sequence (TS.x)-related parameters are derived from related timesequence (rTS.x) values of continuous pressure-related signals of apopulation of pressure recordings.
 17. The method according to claim 1,wherein said formula-based adjustments of time sequence (TS.x)-relatedparameters of said individual time sequence windows of said continuouspressure-related signals relate to creation of factorized time sequence(fTS.x) parameters, said factorized time sequence (fTS.x) parametersselected from the group of: factorized mean value of starting diastolicminimum pressure of a time sequence window (fTS.MeanP_(min1)),factorized standard deviation of mean value of starting diastolicminimum pressure of a time sequence window (fTS.MeanP_(min1) _(—) STD),factorized mean value of systolic maximum pressure of a time sequencewindow (fTS.MeanP_(max)), factorized standard deviation of mean value ofsystolic maximum pressure of a time sequence window (fTS.MeanP_(max)_(—) STD), factorized mean amplitude of a time sequence window(fTS.MeandP), factorized standard deviation of mean amplitude of a timesequence window (fTS.MeandP_STD), factorized mean latency of a timesequence window (fTS.MeandT), factorized standard deviation of meanlatency of a time sequence window (fTS.MeandT_STD), factorized mean risetime coefficient of a time sequence window (fTS.MeanRT), factorizedstandard deviation of mean rise time coefficient of a time sequencewindow (fTS.MeanRT_STD), factorized mean wave duration of a timesequence window (fTS.MeanWD), factorized standard deviation of mean waveduration of a time sequence window (fTS.MeanWD_STD), factorized meansingle wave pressure of a time sequence window (fTS.Mean_(SW)P),factorized standard deviation of mean single wave pressure of a timesequence window (fTS.Mean_(SW)P_STD), factorized mean value of diastolicminimum pressure difference of a time sequence window(fTS.MeanDiff_P_(min)), factorized standard deviation of mean value ofdiastolic minimum pressure difference of a time sequence window(fTS.MeanDiff_P_(min) _(—) STD), factorized mean value of systolicmaximum pressure difference of a time sequence window(fTS.MeanDiff_P_(max)), factorized standard deviation of mean value ofsystolic maximum pressure difference of a time sequence window(fTS.MeanDiff_P_(max) _(—) STD), factorized mean amplitude difference ofa time sequence window (fTS.MeanDiff_dP), factorized standard deviationof mean amplitude difference of a time sequence window(fTS.MeanDiff_dP_STD), factorized mean latency difference of a timesequence window (fTS.MeanDiff_dT), factorized standard deviation of meanlatency difference of a time sequence window (fTS.MeanDiff_dT_STD),factorized mean rise time coefficient difference of a time sequencewindow (fTS.MeanDiff_RT), factorized standard deviation of mean risetime coefficient difference of a time sequence window(fFS.MeanDiff_RT_STD), factorized mean wave duration difference of atime sequence window (fTS.MeanDiff_WD), factorized standard deviation ofmean wave duration difference of a time sequence window(fTS.MeanDiff_WD_STD), factorized standard deviation of mean single wavepressure difference of a time sequence window(fTS.MeanDiff_Mean_(SW)P_STD), factorized amplitude of the mean wave ofa time sequence window (fTS.MeanWavedP), factorized latency of the meanwave of a time sequence window (fTS.MeanWavedT), and factorized risetime coefficient of the mean wave of a time sequence window(fTS.MeanWaveRT).
 18. The method according to claim 17, wherein saidformula-based adjustments of time sequence (TS.x)-related parametersrelates to multiplication of the pressure scale of said individual timesequence windows of said pressure-related signal with a given constantfactor value derived from the related time sequence (rTS.x) parameters.19. The method according to claim 17, wherein said formula-basedadjustments of time sequence (TS.x)-related parameters relates toadjustment of the pressure scale of said individual time sequencewindows of said pressure-related signal according to a formula-basedrelationship derived from the related time sequence (rTS.x) parameters.20. The method according to claim 1, wherein said pressure-relatedsignals relate to human or animal body pressures elected from one ormore of: intracranial pressure, arterial blood pressure, cerebrospinalfluid pressure, cerebral perfusion pressure, ocular pressure,gastrointestinal pressure, urinary tract pressure, or any type of softtissue pressure.